JP4044606B2 - Personal digest distribution apparatus, personal digest distribution method and program - Google Patents

Description

  The present invention relates to a personal digest distribution apparatus, a personal digest distribution method, and a program.

  In recent years, portable information terminals (portable terminals) such as PDAs (Personal Digital Assistants) and mobile phones have become widespread. In particular, third-generation mobile terminals have started services for receiving moving images as well as text and still images at a maximum of 384 Kbps. Furthermore, it is expected that a speech-to-speech function will be implemented in the future.

  By the way, there is a service for transmitting information about sports to a portable terminal. This service informs the user of information about the progress of sports matches such as baseball and soccer, and breaking news of the game results. The user of the service can know the situation and result of the game even when the user is unable to view the television or the like due to reasons such as moving.

  The service edits the highlights of the game, creates a digest of the game, and creates information that is sent to the mobile terminal. The digest information is composed of a character string and a scene corresponding to the character string, for example, “Team G is reversed due to a timely hit by a player T of team G3-1, team C3-1 and team G”.

JP 2001-275058 A

  By the way, a user who wants to distribute a personal digest of sports generally has a preference such as a favorite team or a favorite player. Since many portable terminals are owned by individuals, it is conceivable to reflect the preference of each user in the personal digest.

  Some users who have a preference for sports games want to distribute a scene that their favorite team has an advantage, but do not want to distribute an unfavorable scene such as getting a score. On the other hand, there are some who do not want to distribute a scene that is advantageous to a specific team or player, but want to distribute an unfavorable scene. A person who wants to distribute a scene that is disadvantageous for a specific team or player is called an anti-fan. The conventional personal digest distribution system cannot generate a digest by judging an advantageous scene or an unfavorable scene corresponding to the user's preference.

  Also, in sports games, although there was no scoring, there are scoring opportunities and defeated scenes. Some users wish to distribute scenes where their favorite players and teams get a chance. However, since the conventional personal digest determines the importance of the scene based on the result of the score or victory, the distribution of the chance scene could not be met.

  Furthermore, in sports matches, one of the two teams generally attacks and the other defends against the attack. In other words, the game has at least two elements, attack and defense. For example, when distributing a baseball broadcast personal digest, a user who is a fan of a team with excellent pitcher power may desire to see more scenes related to team defense. In addition, a fan of a team that is superior in striking power may desire to see more scenes related to the attack. However, the conventional personal digest device cannot set the degree of distribution desired for each video element, and there is room for improvement to fully reflect the user's preference.

  The present invention has been made in view of the above points, and provides a personal digest distribution device, a personal digest distribution method, and a program for causing a computer to execute the method to distribute a personal digest more reflecting the user's preference. The purpose is to provide. It also meets the demands of users who want to deliver scenes that are particularly disadvantaged, called anti-fans, and can handle not only the results of the game but also the chance scenes to be delivered as digest scenes. An object of the present invention is to provide a digest distribution apparatus, a personal digest distribution method, and a program for causing a computer to execute the method.

  In order to solve the above-mentioned problems and achieve the object, the invention according to claim 1 is a personal that creates and distributes a personal digest for a user who has requested distribution from video composed of a plurality of continuous scenes. A digest distribution device for generating a digest scene that is a scene on the video that is a candidate for a scene for generating the personal digest, and a metadata that indicates an event that has occurred on the digest scene; A preference item registration unit that registers a preference item that a user desires to distribute with respect to the video, and a degree that the user desires to distribute with respect to the preference item that is registered in the preference item registration unit for each element in the video Desired degree registration means to be registered in the preference item registered in the preference item registration means Registration means for registering whether the user likes or dislikes each element in the video, and the various elements that vary according to at least one or more metadata in each digest scene Video content analysis unit for calculating the importance parameter for each preference item, the importance parameter for the various elements for each preference item in the digest scene calculated by the video content analysis unit, and the desired Based on whether the user likes or dislikes the preference item registered for each element in the video by the degree registration means, and the degree of distribution request for each element in the video, The importance level of each digest scene is calculated for each user, and each digest scene is ranked according to the calculated importance level. And a scene selection unit that selects said personal digest for the user who requested the distribution with the digest scene of ranking higher, characterized in that it comprises for.

  Further, the invention according to claim 2 is the personal digest distribution apparatus according to claim 1, wherein the importance of the digest scene is a positive or negative value determined in advance for each preference item. It is calculated by multiplying the parameter.

The invention according to claim 3 is the personal digest distribution device according to claim 2, wherein the scene selection unit calculates the importance of each digest scene for each user by the following equation:

Importance = (Degree of distribution desired for each element * flag * Importance parameter for each element) +... + (Degree of distribution desired for each element * flag * Importance parameter for each element)
However, flag is a flag indicating whether the user likes or dislikes the preference item, likes: 1, hates: -1, default: 1.

It is characterized by that.

  According to a fourth aspect of the present invention, in the personal digest distribution device according to any one of the first to third aspects, when the video is a video of a sport game, the desired degree registration means The degree is registered as various elements in the video according to at least one of a defensive element, an aggressive element, and a situational element.

  The invention according to claim 5 is for creating and distributing a personal digest for a user who has requested distribution from a video composed of a plurality of continuous scenes. A personal digest distribution method in a personal digest distribution device comprising a video section, a video content analysis section, and a scene selection section, wherein the digest scene generation section uses the one scene on the video as a candidate scene for generating the personal digest. A step of generating metadata indicating a certain digest scene and an event that has occurred on the digest scene, a step of registering a preference item that a user desires to distribute regarding the video by the preference information registration unit, and the preference information registration Registered in the preference item registration function Regarding the preference item, the user likes the step of registering the degree to which the user desires distribution for each of various elements in the video, and the preference item registered in the preference item registration function by the preference information registration unit. The step of registering whether or not to dislike each element in the video, and about the various elements that vary according to at least one or more metadata in each digest scene by the video content analysis unit Calculating the importance parameter for each preference item, the importance parameter for the various elements for each preference item in the digest scene calculated by the video content analysis function by the scene selection unit, and The preference item registered for each element in the video by the desired degree registration function The importance of each digest scene is calculated for each user based on whether the user likes or dislikes and the degree of distribution desired for each element in the video, and the calculated Ranking each digest scene according to importance, and selecting the personal digest for the user who requested distribution using the digest scene at the top of the ranking.

  According to a sixth aspect of the present invention, there is provided a program for causing a computer to execute a process of creating and distributing a personal digest for a user who has requested distribution from a video composed of a plurality of continuous scenes. A digest scene generation function for generating a digest scene that is one scene on the video that is a candidate for a scene to generate a digest and metadata indicating an event that has occurred on the digest scene, and a user wants to distribute the video A preference item registration function for registering preference items to be registered, and a preference degree registration function for registering, for each of the various elements in the video, a degree to which a user desires distribution regarding the preference items registered in the preference item registration function; The user likes or dislikes the preference item registered in the preference item registration function. A preference function for registering each of the various elements in the video, and an importance parameter for the various elements that vary according to at least one or more metadata in each digest scene. Video content analysis function to be calculated for each, the importance parameter for the various elements for each preference item in the digest scene calculated by the video content analysis function, and various elements in the video by the desired degree registration function The importance of each digest scene is determined based on whether the user likes or dislikes the preference item registered every time and the degree of distribution request for each element in the video. Each digest scene is ranked according to the calculated importance, and the highest ranking Characterized in that to execute a scene selection function of selecting the personal digest for the user who requested the distribution with the digest scene, to the computer.

  According to the present invention, a user who is a fan of a team with excellent pitcher power is able to defend the team when delivering a personal digest of a baseball broadcast, for example, by setting the degree of distribution desired for each element in the video. The scene fans can see more scenes, and the fans of the team with excellent striking power can see more scenes related to attacks. In addition, by registering whether a user likes or dislikes a preference item for each element in the video, for example, a person who wants to distribute a scene that is disadvantageous for a specific team or player (so-called anti-virus) A personal digest can be generated corresponding to the fan's preference. As described above, in order to register the preference item that the user wants to deliver, the degree of delivery hope, and whether the user likes or dislikes the preference item for each element of the video, the user There is an effect that it is possible to provide a personal digest distribution apparatus that distributes a personal digest that more reflects the user's preference.

  DESCRIPTION OF EXEMPLARY EMBODIMENTS Hereinafter, an embodiment of a personal digest delivery apparatus, a personal digest delivery method, and a program for causing a computer to execute the method according to the present invention will be described in detail with reference to the drawings.

  FIG. 1 is a block diagram for explaining a personal digest distribution apparatus according to an embodiment of the present invention. The personal digest distribution apparatus according to the present embodiment is a personal digest distribution apparatus that creates and distributes a digest from a video composed of a plurality of continuous scenes. The personal digest distribution device according to the present embodiment is a digest that generates metadata and a digest scene that indicate events (events) that occur on a game relay video (sports video) of sports (baseball in the present embodiment). A scene generation unit 101, a metadata database (metadata DB) 105 for storing generated metadata, and a moving image DB 103 for storing digest scenes are provided.

  In addition, the personal digest distribution apparatus according to the present embodiment includes a target registration unit that registers a target that the user desires to distribute with respect to the sports video S, and a degree that the user desires to distribute with respect to the registered target for each video element. A preference information registration unit 109 which is a desired degree registration unit to be registered, and a personal digest creation unit 100 which selects a digest scene in the sports video S based on the registered target and the degree of distribution desired and creates a personal digest. I have.

  The preference information registration unit 109 registers the degree of distribution request according to at least one of defense, attack, and situation. In this embodiment, since the sports video S is used as a baseball broadcast video, the defense is particularly referred to as defense.

  The personal digest creation unit 100 according to the present embodiment selects the digest scene based on the importance, the video content analysis unit 107 that determines the importance of each digest scene of the sports video S according to the defense, the attack, and the situation. And a scene selection unit 111 for performing the operation.

  Furthermore, the personal digest distribution apparatus according to the present embodiment includes a user DB 113 that stores information registered in the preference information registration unit 109. Hereinafter, among the above-described configurations, the preference information registration unit 109, the digest scene generation unit 101, the video content analysis unit 107, and the scene selection unit 111 will be described in order.

[Preference information registration department]
The preference information registration unit 109 is an object registration unit for registering an object (a favorite team in the present embodiment) that the user desires to distribute regarding the sports video S, and an event (home run) that the user desires to distribute regarding the registered object. It functions as a desired degree registration means for registering the degree of desired delivery of the reversal, etc.). In addition, the preference information registration unit 109 according to the present embodiment can also register an event (an event that the user wants to see) that the user wants to distribute for the sports video S.

The user, for example, on the UIF (User Inter Face), the target of preference (the team he is interested in) and the event (the scene he wants to see) (the target team and the event are collectively described as a preference item) Are registered in the preference information registration unit 109.
Interested team (Fav_team)
Events you want to see (Fav_event)

  For teams that are interested, the user registers teams that want to get more information. Also, for the event that you want to see, you register the event that you want to see, such as HR and reverse scene.

  Furthermore, in the personal digest distribution apparatus according to the present embodiment, the preference information registration unit 109 registers the degree of distribution preference of the user's preference items. FIG. 2 shows attributes specified by the user on the UIF in order to register the degree of distribution preference of the preference item.

  As illustrated in FIG. 2, the preference information registration unit 109 registers whether the user likes or dislikes the target as Fav_flag. Fav_flag is set to 1 when the user likes the target of the preference item, and is set to -1 when the user does not like it. The default of Fav_flag is 1. In addition, the preference information registration unit 109 registers the degree of distribution request according to at least one of a defensive element, an aggressive element, and a situation element.

  Among the attributes shown in FIG. 2, the degree of distribution request according to the attack level that is an aggressive element is registered as Fav_offense. In Fav_offense, the user registers how much of an aggressively important scene is to be viewed within a range of 1 to α. The default for Fav_offense is 1. Also, the degree of distribution request according to the defensive level, which is a defensive element, is registered as Fav_defense. In Fav_defense, the user registers himself / herself in the range of 1 to β to see how much defensive scenes he / she wants to see. The default for Fav_defense is 1. Further, the degree of distribution request according to the situation element is registered as Fav_chance. In Fav_chance, the user registers himself in the range of 1 to γ how much the scene that was a chance is to be viewed.

For example, a user who likes Team T very much, strongly desires to deliver an advantageous scene on Team T's attack and defense side, and strongly desires to deliver an opportunity scene for Team T is as follows: By registering each attribute in, it is possible to receive the distribution of the digest scene as desired.
(Fav_name, Fav_flag, Fav_offense, Fav_defense, Fav_chance)
= ("Team T", 1, α, β, γ)

In addition, for example, a user who is very disliked by Team G and strongly desires delivery of a disadvantageous scene on Team G's attack side and defense side, and who is not interested in the opportunity scene for Team G is as follows: By registering each attribute in, it is possible to receive the distribution of the digest scene as desired.
(Fav_name, Fav_flag, Fav_offense, Fav_defense, Fav_chance)
= ("Team G", -1,1,1,1)

  In the present embodiment described above, an example is shown in which the target is a team, and a team T fan or an anti-team G fan distributes a personal digest desired to be distributed. However, the present invention does not limit the target to a team, but can also register a player to meet the demand of a specific player's fan or a specific player's anti-fan.

[Digest scene generator]
The digest scene generation unit 101 is configured as an information terminal device that edits a moving image of a video shot in a baseball field. When an operator presses a registration button on a screen (not shown) of the information terminal, metadata corresponding to one play video scene is generated. The generated video scene is called a digest scene, and the metadata is called event information I in the present embodiment. A digest scene is a scene that is a candidate for a scene that generates a personal digest, and an event indicates an event that has occurred on each digest scene. When events are grouped for each digest scene, this group is called an event list.

  FIG. 3 is a diagram for explaining an event. Each row of the table shown in FIG. 3 corresponds to one of the basic events that have occurred on the digest scene, and indicates event information that is event information. The event information is represented by an event type name [attribute 1, attribute 2,... Attribute n], and the attribute i is set depending on the event type (start at bat, advance, out, etc.).

  Event information is created for each digest scene. A digest scene is created for each video (one play scene) that is a digest scene candidate. In the present embodiment, the digest scene is created based on a video including a batter's batter start event to an event just before the next batter's batter start.

FIG. 4 is a diagram for explaining the relationship between the digest scene and the event. In the figure, a plurality of events a, b, c... Exist in one play indicated by P. Examples of the events a, b, and c include the following contents.
Event a Team K / Team D 6th reverse R player: Start at bat Event b Team K / Team D 6th reverse R player: Center crossing home run
R player, S player addition event c Team K, team D 6th back M player (next batter): Additional points

  The event immediately before the addition by the M player of the event c from the start of the bat of the R player of the event a includes the event from the start of the batting of the R player to immediately before the start of the bat of the M player of the next batter. Therefore, the digest scene generation unit 101 edits the scene from event a to event c in 15 seconds to create a digest scene. Note that one digest scene has a plurality of events that occur from the start of the bat to the start of the next batter's bat, and the digest scene and the event correspond to a one-to-many relationship. A set in which events included in each digest scene are arranged in time series is called an event list, and is indicated by R in the figure.

[Video content analysis section]
The video content analysis unit 107 inputs the event information I stored in the metadata DB 105 and calculates the importance of each digest scene based on the event information I. The importance is calculated for each of the three elements of attack, defense, and situation. Each importance is calculated based on the importance parameter. The importance parameters used to calculate the importance of attack, defense, and situation are referred to as an attack level, a defense level, and a situation level, respectively.

  FIG. 5 is a diagram showing characteristics of attack level, defense level, and situation level, and calculation timing. The defensive level is an importance parameter that considers all events on the defensive side including events related to pitchers. Each importance parameter indicates the advantage or disadvantage of the attacking team and the defensive team in each digest scene, and whether the situation is good or bad.

Next, processing for calculating an attack level, a defense level, and a situation level will be described. The importance parameter is calculated by the following procedure.
(1) Importance parameter calculation according to at least one of defense, attack, and situation for each event (2) Importance parameter for each event is calculated for each team

(1) Parameter calculation based on events FIG. 6 shows a list of correspondences between the importance level parameters of the attack level, the defense level, and the situation level, and the events used to obtain the value of each importance level parameter. It is a figure. Each importance parameter is obtained for each event in the rightmost column of the table, and one event is referred to only for obtaining one kind of importance parameter, and is not used for obtaining a plurality of importance parameters. The obtained importance parameter is stored in the metadata DB 105 as attribute information of the corresponding event.

  The event includes an event that makes the calculated importance parameter value positive and an event that makes it negative. For example, the attack level is raised by the occurrence of an event advantageous to the attacker such as goodbye, reversal, and preemptive. It is also lowered by events that are advantageous to the defensive side, such as overrun and defensive defensive. The defense level is increased by an event advantageous to the defender such as triple play, triple strikeout, and good catching ball, and is lowered by occurrence of an event advantageous to the attack side such as an error.

  The situation level is calculated based on the attribute information attached to the event information of the first event (usually the start of a bat) in each digest scene. The attribute information is, for example, information on the runner who is on the heel at the start of the batting and information on the batting order of the batter (whether it is cleanup or lower batting). The video content analysis unit 107 refers to the attribute information and sets the situation level higher when the score or the possibility of reversal is high.

  According to the processing described above, the importance of each event belonging to the digest scene can be determined. In addition, it is possible to determine how important each digest scene is from the viewpoint of attack or defense. Furthermore, from the viewpoint of the situation, it is possible to determine the degree of highlight of the digest scene (so-called good scene).

(2) Calculate the importance parameter for each event for each team Next, the video content analysis unit 107 responds to at least one of the defense level, the attack level, and the situation level calculated for each event by the processing of (1). Based on the importance parameter, the importance parameter for each event is calculated for each team. The importance parameter for each event team is determined as follows.

  The video content analysis unit 107 sets the attack level of the event occurring on the digest scene as it is to the attack side of the team on the attack side in the digest scene. On the other hand, for the defensive team, the attack level of the event is a value obtained by multiplying the attack level of the team on the attacking side by -1. Similarly, the defense level of the event occurring on the digest scene is set to the attack level as it is for the team on the defense side in the digest scene. On the other hand, for the attacking team, a value obtained by multiplying the defensive level of the team on the defensive side by -1 is set as the defensive level of the event. As described above, the attack level and the defense level of each event are calculated for each team.

  The situation level for each team is calculated as the importance parameter of the start event of the digest scene. The situation level will not change, regardless of the team's attack and defense.

  According to the above processing, it is possible to determine the importance of each event for attack, defense, and situation for each team.

  Further, in the present embodiment, the attack level of each event is based on an event relating to a registered player (registered player) when a specific player is registered as a target instead of a team as in the above-described embodiment. The defense level and the situation level can be calculated for each player.

  The defense level, attack level, and situation level for each player are set so that, for example, if the registered player is involved in a goodbye or reverse event, the attack level will be increased, and the attack level of the registered player will be multiplied by -1 It can be calculated by setting the attack level. In addition, when an event occurs such that the registered player takes the opponent out, or strikes out, the defense level is set to increase, and the defense level of the opponent player is set by multiplying the defense level of the registered player by -1. Can be calculated. Further, the situation level can be calculated based on an event when the batter at the start of the batting of the digest scene is a registered player.

[Scene selection section]
The scene selection unit 111 is configured to select a digest scene based on a target and an event including the target registered in the preference information registration unit 109, and the degree of distribution desired. For this reason, the scene selection unit 111 calculates the importance of each digest scene for each team for each user. The importance of each digest scene for each team and each user is calculated as follows.

The scene selection unit 111 according to the present embodiment uses the event registered in the preference information registration unit 109 in addition to the importance parameter for each event calculated by the video content analysis unit 107 for each team. The importance for each user is calculated. That is, the importance parameter (Si.Tj.Pk) regarding the attack, defense, and situation of the team Tj of the digest scene Si for the user who registered the event to be viewed is calculated by the following equation.
Si. Tj. Pk = Func (Ek, Tj, Pk, Fav_event)
Si: Digest scene Tj: Team identifier (home team or away team)
Pk: identifier indicating attack, defense, and situation level Ek: set of events belonging to the digest scene Si Fav_event: event to be viewed Func: set of events belonging to the digest scene Si, team identifier (team ID), importance to be calculated Using the parameter elements (attack, defense, and situation) and the type name of the event that the user wants to see as arguments, calculate the importance parameter corresponding to either attack, defense, or situation in the digest scene Si for each user or team. Function

Next, the scene selection unit 111 determines the importance parameter (corresponding to any one of attack, defense, and situation) of the digest scene Si calculated for each user and each team, the team registered in the preference information registration unit 109, and The degree of importance of each digest scene is calculated for each user based on whether the user likes or dislikes the registered team and the degree of desire for distribution. Since the calculated importance is directly used for selecting a digest scene constituting the personal digest, it is also referred to as a final importance. The formula for calculating the final importance is as follows.

Final importance of scene Si =
(Fav_offense * flag * Si.Tj.Pk (attack level))
+ (Fav_defense * flag * Si.Tj.Pk (defense level))
+ (Fav_chance * flag * Si.Tj.Pk (status level))

Fav_offense: Degree of desirability of aggressive elements
Fav_defense: Degree of desirability of distributing defensive elements
Fav_chance: Degree of distribution of situational elements
flag: Flag indicating whether to like or dislike a preference item that is an object or an event, like: 1, hate: -1, default: 1
Si: Video scene
Tj: target identifier

  The scene selection unit 111 calculates the importance of each digest scene according to the above formula. Then, the digest scenes are ranked according to the calculated importance, and a personal digest for the user who has requested distribution is created using the digest scenes at the top of the ranking.

  As described above, the target is set to a specific player, and Si. Tj. If the importance parameter for each element such as each user of the digest scene Si, each player, attack, defense, and situation is substituted into the item of importance parameter (Pk), this embodiment determines the final importance of a specific player. Can be calculated.

  The configuration described above operates as follows. That is, the digest scene generation unit 101 inputs the sports video S and creates a digest scene that is an average video of about 15 seconds. The created digest scene is accumulated in the moving image DB 103. In addition, information indicating a plurality of events that occur on each created digest scene is accumulated in the metadata DB 105 as metadata.

  The video content analysis unit 107 reads the event information stored in the metadata DB 105 and obtains the importance parameter P1 of each event. The importance parameter P1 of each event may be set in advance. Further, it may be calculated using a preset arithmetic expression. Further, the video content analysis unit 107 calculates the importance parameter P2 of each event for each team using the importance parameter P1 of each event. Both the importance parameter P1 and the importance parameter P2 are stored in the metadata DB 105.

  By the way, in the preference information registration unit 109, information related to the user's preference is set and registered in advance by the user himself from the UIF of the mobile terminal. When creating a personal digest at the timing when the user wishes to deliver a personal digest, the scene selection unit 111 receives a delivery request and requests a preference item related to the preference of the user who requested delivery from the user DB 113. Desired event) and preference attributes (whether they like or dislike the object, the degree of desire for distribution, etc.) are read out.

  Then, the scene selection unit 111 calculates the importance parameter of the digest scene for each user and each team based on the information f and the importance parameter P2. Subsequently, the final importance parameter is calculated based on the calculated importance parameter of the digest scene for each user and each team. Furthermore, the digest scene is ranked based on the importance parameter, and a personal digest is created and distributed to the user who has requested distribution.

  FIG. 7 and FIG. 8 are flowcharts for explaining a personal digest distribution method performed in the personal digest distribution apparatus described above. FIG. 7 is a flowchart for explaining a process performed before creating a personal digest, and FIG. 8 is a flowchart for explaining a method for creating a personal digest based on user preferences.

  As shown in FIG. 7, the personal digest distribution apparatus first determines whether or not a sports video has been input (step S701). If no sports video is input (step S701: No), the system waits until a sports video is input. When a sports video is input (step S701: Yes), the digest scene generation unit 101 generates metadata together with the digest scene (steps S702 and 703). The digest scene and metadata generation process is continued until all of the sports video is processed. The generated digest scene is stored in the moving image DB 103, and the metadata is stored in the metadata DB 105.

  The digest scene generation unit 101 determines whether the generation of the digest scene and metadata has been completed (step S704). If it is determined that the process has not ended (step S704: No), the generation of the digest scene and metadata is continued until the process ends.

  If the digest scene generation unit 101 determines that the generation of the digest scene and metadata has ended (step S704: Yes), the video content analysis unit 107 includes an event (represented by metadata) included in the digest scene. Is calculated (step S705). Then, an importance parameter for each team is calculated for each event from the importance parameters calculated for each event (step S706). The importance parameter P1 for each event (event unit) and the importance parameter P2 for each event unit for each team are stored in the metadata DB 105 together with the metadata.

  Next, as shown in FIG. 8, the scene selection unit 111 determines whether there is a user's desire to distribute a personal digest (step S801). As a result of the determination, if the distribution request is not made (step S801: No), the process waits until the distribution request is made.

  Further, the scene selection unit 111 acquires the preference information of the user who desires distribution from the preference information registration unit 109 (step S802). Then, the importance level parameter P1 and the importance level parameter P2 stored in the metadata DB 105 are read, and the importance level of each digest scene is calculated for each user who wishes to distribute by team (step S803). Subsequently, the final importance of each digest scene is calculated for each user based on the calculated importance of the digest scene for each team (step S804). Further, the digest scenes are ranked in descending order of importance, and a predetermined number is selected from the highest ranked ones to create a personal digest (step S805). The scene selection unit 111 distributes the created personal digest to the user who desires distribution, and ends the process (step S806).

  Note that a program for causing a computer to execute the personal digest distribution method of the present embodiment is an installable or executable file that can be read by a computer such as a CD-ROM, a flexible disk (FD), or a DVD. Recorded on a recording medium and provided. In addition, a program for causing a computer to execute the personal digest distribution method may be stored on a computer connected to a network such as the Internet and provided by being downloaded via the network.

It is a block diagram for demonstrating the personal digest delivery apparatus of embodiment of this invention. It is a figure which shows the attribute which a user designates on UIF in order to register the degree of the delivery hope of the preference item of one embodiment of this invention. It is a figure for demonstrating the event of one embodiment of this invention. It is a figure for demonstrating the relationship between the digest scene of one embodiment of this invention, and an event. It is a figure which shows the characteristic of the attack level of one embodiment of this invention, the defense level, the situation level, and the timing of calculation. It is the figure which showed as a table | surface the correspondence with each severity parameter of the attack level of the embodiment of this invention, a defense level, and the event used for calculating | requiring the value of each importance parameter. . It is a flowchart for demonstrating the delivery method of the personal digest of one embodiment of this invention. It is another flowchart for demonstrating the delivery method of the personal digest of one embodiment of this invention.

Explanation of symbols

100 Personal Digest Creation Unit 101 Digest Scene Generation Unit 103 Video DB
105 Metadata DB
107 Video content analysis unit 109 Preference information registration unit 111 Scene selection unit P1 Importance parameter P2 Importance parameter S Sports video

Claims (6)

A personal digest distribution device that creates and distributes a personal digest for a user who has requested distribution from video composed of a plurality of continuous scenes,
A digest scene generating unit that generates a digest scene that is one scene on the video that is a candidate for a scene for generating the personal digest, and metadata indicating an event that has occurred on the digest scene;
A preference item registration means for registering a preference item that the user desires to distribute with respect to the video;
Regarding the preference item registered in the preference item registration means, a desired degree registration means for registering a degree of a user's desire for distribution for each of various elements in the video,
Registration means for registering whether the user likes or dislikes the preference item registered in the preference item registration means for each of various elements in the video;
A video content analysis unit that calculates, for each preference item, an importance parameter for the various elements that vary according to at least one or more metadata in each digest scene;
Using the importance parameter for each element for each preference item in the digest scene calculated by the video content analysis unit and the preference item registered for each element in the video by the desired degree registration means The importance level of each digest scene is calculated for each user based on whether the user likes or dislikes and the degree of distribution desired for each element in the video. A scene selection unit that ranks each digest scene at a time, and selects the personal digest for a user who has requested distribution using the digest scene at the top of the ranking;
A personal digest distribution apparatus comprising: The importance of the digest scene is calculated by multiplying the importance parameter for the various elements by a positive or negative value determined in advance for each preference item.
The personal digest distribution apparatus according to claim 1. The scene selection unit calculates the importance of each digest scene for each user according to the following equation:

Importance = (Degree of distribution desired for each element * flag * Importance parameter for each element) +... + (Degree of distribution desired for each element * flag * Importance parameter for each element)
However, flag is a flag indicating whether the user likes or dislikes the preference item, like: 1, dislike: -1, default: 1

The personal digest distribution apparatus according to claim 2, wherein: When the video is a sports game relay video, the desired degree registration means determines the degree of distribution according to at least one of a defensive element, an aggressive element, and a situational element as various elements in the video. sign up,
The personal digest distribution apparatus according to any one of claims 1 to 3. A personal digest for a user who has requested distribution from a video composed of a plurality of continuous scenes is generated and distributed, and a digest scene generation unit, a preference information registration unit, a video content analysis unit, and a scene selection unit A personal digest delivery method in a personal digest delivery device comprising:
Generating a digest scene that is one scene on the video that is a candidate for a scene for generating the personal digest by the digest scene generation unit and metadata indicating an event that has occurred on the digest scene;
Registering a preference item that a user wishes to distribute with respect to the video by the preference information registration unit;
A step of registering, for each of various elements in the video, a degree of a user's desire to distribute the preference item registered in the preference item registration function by the preference information registration unit;
Registering, for each of various elements in the video, whether the user likes or dislikes the preference item registered in the preference item registration function by the preference information registration unit;
Calculating importance parameters for the various elements that vary according to at least one or more metadata in each digest scene by the video content analysis unit for each preference item;
The importance parameter for the various elements for each preference item in the digest scene calculated by the video content analysis function by the scene selection unit and the various elements in the video by the desired degree registration function. Based on whether the user likes or dislikes the preference item and the degree of distribution desired for each element in the video, the importance of each digest scene is calculated for each user, Ranking each digest scene with the calculated importance, and selecting the personal digest for the user who requested distribution using the digest scene at the top of the ranking;
A personal digest distribution method comprising: A program for causing a computer to execute a process of creating and distributing a personal digest for a user who has requested distribution from video composed of a plurality of continuous scenes,
A digest scene generation function for generating a digest scene that is one scene on the video that is a candidate for a scene for generating the personal digest and metadata indicating an event that has occurred on the digest scene;
A preference item registration function for registering a preference item that the user wishes to distribute regarding the video;
Regarding the preference item registered in the preference item registration function, a desired degree registration function for registering a degree of a user's desire for distribution for each of various elements in the video,
A registration function for registering whether the user likes or dislikes the preference item registered in the preference item registration function for each of various elements in the video,
A video content analysis function for calculating, for each preference item, an importance parameter for the various elements that vary according to at least one or more metadata in each digest scene;
Using the importance parameter for each element for each preference item in the digest scene calculated by the video content analysis function and the preference item registered for each element in the video by the desired degree registration function The importance level of each digest scene is calculated for each user based on whether the user likes or dislikes and the degree of distribution desired for each element in the video. A scene selection function that ranks each digest scene at a time, and selects the personal digest for the user who requested distribution using the digest scene at the top of the ranking;
That causes the computer to execute the program.

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