JP2008204007A - Image dictionary generation method, device and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To extract a proper semantic label according to a content without previously selecting a semantic label to be imparted to accurately generate an image dictionary. <P>SOLUTION: Content related information that is text information wherein contents of the video content are explained is acquired from a Web site, the text information related to the content is collected from the Web site based on a content disclosure data or a content creation date, and a word extracted from the content related information, a plurality of words expressing a topic are extracted from the text information as a word set, a Web image related to the word set is collected, a visual pattern related to the topic is generated with the collected Web image as learning data, and the word set and the visual pattern are stored in an image dictionary storage means as the image dictionary. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to an image dictionary generation method, apparatus, and program, and more particularly, to an image dictionary generation method, apparatus, and program for creating an image dictionary using Web information including text information and image information.

  There are the following methods as a conventional image dictionary generation method.

  First, a semantic label to be given for a video is selected. Next, images representing the meaning labels are collected as learning data. Finally, the visual pattern associated with the semantic label is obtained from the learning data. Through the above processing, an image dictionary composed of visual patterns associated with semantic labels related to video can be generated (for example, see Non-Patent Document 1).

In addition, as a method of efficiently collecting learning data, there is a method of collecting images from a website using a word representing a semantic label prepared in advance (see, for example, Non-Patent Document 2).
Y. Wu. BL Tseng and JR Smith, "Ontology-based Multi-Classification Leaning for Video Concept Detection," IEEE International Conference on Multimedia and Expo (ICME), June 2004. Yongqing Sun, Satoshi Shimada, Masashi Morimoto, "Visual pattern discovery using web images". ACM MIR workshop, 2006.

  However, since the image dictionary generation method as shown in Non-Patent Document 1 described above depends on the learning data for the accuracy of the image dictionary related to the video, it takes an enormous amount of time to select the learning data that can be well reflected in the actual video. There is a problem that it takes time and effort.

  In addition, the method of collecting learning data as shown in Non-Patent Document 2 described above includes various kinds of images in the collected web image due to the characteristics of the web information. Therefore, there is a problem that the accuracy of the generated image dictionary is lowered.

  Furthermore, in both conventional methods, it is necessary to select in advance what kind of semantic label is to be given to the video, but in order to select an appropriate semantic label that reflects the video content, time and effort are required. There is a problem that requires.

  The present invention has been made in view of the above points, and can extract an appropriate semantic label according to content and generate an image dictionary with high accuracy without selecting a semantic label to be given in advance. An object is to provide an image dictionary generation method, apparatus, and program.

  In recent years, Web information and TV video have been closely related to information and communication / broadcasting cooperation. Therefore, in order to collect images related to video for which a semantic label is to be added, Web information such as that described in Non-Patent Document 2 is used. It is effective to utilize The present invention improves the accuracy by providing means for automatically extracting an appropriate semantic label according to the content in order to solve the problem of the prior art that the accuracy of the image dictionary is low. Specifically, the following means are used.

  FIG. 1 is a diagram for explaining the principle of the present invention.

The present invention (Claim 1) is an image dictionary generation method for generating an image dictionary that defines a relationship between a semantic label and a visual pattern for giving a semantic level to a video,
A video content related information acquisition unit (step 1) in which the video content related information acquisition means acquires content related information, which is text information describing the content of the video content, from the website;
A text information collecting step (step 2) in which the text information collecting means collects text information related to the content from the website based on the word extracted from the content related information and the content creation date or the content release date, and stores it in the storage means;
A topic extraction step (step 3) in which the topic extraction unit extracts a plurality of words representing the topic from the text information stored in the storage unit as a word set;
The image collection / visual pattern generation means collects a web image related to a word set (step 4), generates a visual pattern related to a topic using the collected web image as learning data (step 5), and the word set and the visual A pattern is stored in the image dictionary storage means as an image dictionary (step 6). An image collection / visual pattern generation step is performed.

According to the present invention (Claim 2), in the topic extraction step (Step 3),
In accordance with the video genre extracted from the content-related information, a video attribute table defining the type of topic to be extracted is stored in the storage means, and each attribute n (n = 1, 2,..., N) of the attribute table is stored. ), The text information stored in the storage means is read and classified by the text information collecting step,
A word set representing a topic related to the attribute n is extracted from the classified text information of the same group.

  FIG. 2 is a principle configuration diagram of the present invention.

The present invention (Claim 3) is an image dictionary generation device for generating an image dictionary defining a relationship between a semantic label and a visual pattern for giving a semantic level to a video,
Video content related information acquisition means 100 for acquiring content related information, which is text information describing the content of video content, from a website;
Text information collecting means 101 for collecting text information related to the content from the website 3 based on the word extracted from the content related information and the content creation date or the content release date, and storing it in the storage means;
Topic extraction means 104 for extracting a plurality of words representing a topic from the text information stored in the storage means as a word set;
Collecting web images related to a word set, generating a visual pattern associated with a topic using the collected web image as learning data, and storing the word set and the visual pattern as an image dictionary in the image dictionary storage means 2 Pattern generation means 105.

The present invention (Claim 4) further includes video attribute storage means storing a video attribute table that defines the type of topic to be extracted according to the video genre extracted from the content related information,
The topic extraction means 104
Means for reading and classifying the text information stored in the storage means by the text information collection means 101 for each attribute n (n = 1, 2,..., N) in the attribute table of the video attribute storage means;
Means for extracting a word set representing a topic related to the attribute n from the classified text information of the same group.

  The present invention (Claim 5) is a program for causing a computer to realize the function according to Claim 3 or 4.

  As described above, according to the present invention, it is possible to select a semantic label to be given in advance by collecting Web information (content related information) related to video and classifying them to extract topics. In addition, since an appropriate semantic label is automatically extracted according to content, an image dictionary can be generated with high accuracy without taking time and effort.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings.

  FIG. 3 shows a system configuration in an embodiment of the present invention.

  The system shown in FIG. 1 includes an image dictionary generation device 1, an image dictionary database 2, and a Web site 3 connected to the Internet.

  The image dictionary generation apparatus 1 acquires web information related to a video from the website 3, and generates an image dictionary related to the video using the acquired web information.

  The image dictionary database 2 receives and stores the image dictionary generated by the image dictionary generation device 1.

  The website 3 is a large number of websites published on the Internet. The published information is composed of a web image to which peripheral text is added and web text information. The website 3 outputs to the image dictionary generation device 1 the web image and the web text information to which the peripheral text is added in response to a request from the image dictionary generation device 1.

  Hereinafter, the image dictionary generating apparatus 1 will be described in detail.

  FIG. 4 shows the configuration of the image dictionary generation apparatus according to an embodiment of the present invention.

  The image dictionary generation apparatus 1 shown in FIG. 1 includes a video content related information acquisition unit 100, a text information collection unit 101, a text information classification unit 102, a video attribute table management unit 103, a video topic extraction unit 104, web image collection and visual patterns. The generation unit 105 is configured.

  The processing in each of the above configurations will be described using a TV program video as an example.

  The video content related information acquisition unit 100 acquires text information describing a certain program video from the website 3 and stores it in storage means (not shown) such as a memory. For example, EPG information of a television image may be acquired from a homepage of a TV program broadcasting station. An example of EPG information is shown in FIG. The EPG information shown in the figure includes broadcast date / time, title, genre, outline, performer, and the like, and the video content related information acquisition unit 100 extracts the video title and broadcast date / time from the EPG information to obtain a text information collection unit. 101, the video genre information of the EPG information is extracted and output to the attribute text information classification unit 102.

  Upon receiving the video title and the broadcast date / time from the video content related information acquisition unit 100, the text information collection unit 101 collects web text information from the website 3 for a limited period before and after the broadcast date / time using the video title as a search condition. And stored in storage means (not shown) such as a memory. The collected web text information is output to the text information classification unit 102. For example, recent web text information for one month before one week before the video broadcast date and three weeks after it may be collected.

  Upon receiving the video genre information from the video content related information acquisition unit 100, the text information classification unit 102 outputs the video genre information to the video attribute table management unit 103. Further, the web text information received from the text information collection unit 101 is classified according to the information of the video attribute table received from the video attribute table management unit 103, and stored in a storage means (not shown) such as a memory. The classified text information is output to the video topic extraction unit 104.

  The video attribute table management unit 103 holds and manages a video attribute table defining topic types to be extracted according to the video genre in a storage unit. The video attribute table management unit 103 has a video attribute table as shown in FIG. The video attribute table holds a video genre and a plurality of attributes related to the video genre. The video attribute table management unit 103 receives the video genre information from the text information classification unit 102 and outputs information of the video attribute table associated with the video to the text information classification unit 102.

  The video topic extraction unit 104 reads the text information stored in the memory in the text information classification unit 102, and extracts the upper word as a topic from the word frequency distribution of the text information by a statistical method. The extracted word related to the topic is output to the web image collection and visual pattern generation unit 104.

  The web image collection and visual pattern generation unit 105 collects related web images from the website 3 using a word related to the topic received from the video topic extraction unit 104 as a search condition. A visual pattern related to the topic is generated by using the collected web images as a learning method. An image dictionary composed of words related to the topic and visual patterns is stored in the image database 2.

  With the above configuration, an image dictionary for video is generated using web information including text and images.

  Next, a basic operation in the image dictionary generation apparatus 1 will be described.

  FIG. 7 is a flowchart of the basic operation of the image dictionary generation apparatus according to an embodiment of the present invention.

  Step 201) In the video content related information acquisition unit 100, certain video computer related information is acquired from the website 3 and stored in storage means (not shown). For example, what is necessary is just to acquire the EPG information of a video as shown in FIG. 5 from the homepage of a TV program broadcasting station.

  Step 202) The text information collection unit 101 collects web text information for a limited period before and after the broadcast date and time from the website 3, using the titles in the video content related information EPG as search conditions. For example, recent web text information for one week before the video broadcasting date and time and three weeks after the broadcasting date is collected and stored in a memory (not shown).

  Step 203) The text information classification unit 102 reads the corresponding video attribute table from the video attribute table management unit 103 according to the video genre extracted from the EPG information by the video content related information acquisition unit 100 in Step 201. When the video genre is “drama”, in the example of FIG. 6, the attribute table (person, place, incident, emotion, social activity) for the drama video of the video attribute table management unit 103 is read.

  Step 204) The text information classifying unit 102 searches the web text information collected in Step 202 and stored in the memory (not shown) under the search condition in which the word corresponding to the attribute n in the video attribute table is added. Re-searching is performed to collect text information related to the attribute n and store it in a memory (not shown). The text information related to the attribute n is collected by performing text matching between the web text and a word corresponding to the attribute n, and collecting the web text as text information related to the attribute n in descending order of similarity. Here, if there are N attributes in the video attribute table, the web text collected in step 202 is classified into a text group for each of the N attributes and stored in a memory (not shown).

  Step 205) The video topic extraction unit 104 reads the first text group with n = 1 for the text group classified by the text information classification unit 102 of the memory (not shown), and performs the following processing.

  Step 206) The video topic extraction unit 104 extracts a topic from the text group related to the attribute n. The processing contents will be described with reference to the flowchart of FIG.

  Step 301) The video topic extraction unit 104 reads the web text information related to the attribute N stored in the memory (not shown) in Step 204.

  Step 302) In the read web text information, Stopping words such as “to”, “to” and “to” are deleted.

  Step 303) Using the text information processed in step 302, similar words are integrated. As an example of the attribute n “person” relating to the drama video, the words represented in the text information are “Mr. AAA”, “Mr. T”, “Daughter”, “Friend”, “Mr. C”, and storage means ( Referring to the personal relationship information of the drama video stored in (not shown), similar words are integrated to generate a word set and stored in a memory (not shown). FIG. 9 is an example of personal relationship information of a drama video stored in advance in storage means (not shown). The person relation information of the drama may be read in advance from the homepage of the TV broadcast station and stored in the storage means. The word sets generated according to FIG. 9 are {AAA, daughter}, {friend, T}, and {C}.

  Step 304) Each word set processed in step 303 is read from a memory (not shown), and the frequency of each word set is calculated.

  Step 305) Of the word set frequencies calculated in Step 304, the top M word sets with the highest frequency are extracted as topics related to the attribute n and stored in a memory (not shown).

  Step 207) The web image collection / visual pattern generation unit 105 sets m = 1 and processes the first topic stored in the memory (not shown) in Step 206.

  Step 208) The web image collection / visual pattern generation unit 105 performs web image collection from the website 3 using a word set read from a memory (not shown) as a search condition. A visual module related to the word set is extracted from the collected image, and the word set and the visual module are combined and stored in a memory (not shown). Here, as an example of extracting a common visual module, the method of Non-Patent Document 2 is used to obtain an appropriate recognition function from an image acquired from a website using a word as a collection condition. Just create a module.

  Step 209) In all word sets, it is determined whether or not the processing in the above step 208 has been performed. If not, m = m + 1 is set, and the processing returns to step 208. Otherwise, go to Step 210.

  Step 210) It is determined whether or not the processing of Steps 206 to 209 has been performed on the text information regarding all the attributes. If not, n = n + 1 is set and the processing returns to Step 206. Otherwise, the process proceeds to step 211.

  Step 211) A set of a word set and a visual pattern corresponding to the word set generated in Step 208 and stored in a memory (not shown) is read out from the video and stored in the image dictionary database 2 as an image dictionary. Store.

  Note that the operation of the image dictionary generation apparatus in the above embodiment can be constructed as a program, installed in a computer used as the image dictionary generation apparatus, executed, or distributed via a network.

  Further, the constructed program can be stored in a computer-readable recording medium and installed or distributed in the computer.

  The present invention is not limited to the above-described embodiment, and various modifications and applications can be made within the scope of the claims.

  The present invention can be used for image processing including an image DB system and a video DB system.

It is a figure for demonstrating the principle of this invention. It is a principle block diagram of this invention. 1 is a system configuration diagram according to an embodiment of the present invention. It is a block diagram of the image dictionary production | generation apparatus in one embodiment of this invention. It is the EPG information of the image | video in one embodiment of this invention. It is an example of the video attribute table management part for every video genre in one embodiment of this invention. It is a flowchart of the basic operation | movement of the image dictionary production | generation apparatus in one embodiment of this invention. It is a flowchart of the video topic extraction process regarding the attribute in one embodiment of this invention. It is an example of the person relationship figure in one embodiment of this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Image dictionary production | generation apparatus 2 Image dictionary database 3 Website 100 Video content related information acquisition means, Video content related information acquisition part 101 Text information collection means, Text information collection part 102 Text information classification part 103 Video attribute table management part 104 Topic extraction Means, video topic extraction unit 105 image collection / visual pattern generation unit, image collection / visual pattern generation unit

Claims (5)

An image dictionary generation method for generating an image dictionary that defines a relationship between a semantic label and a visual pattern for giving a semantic level to a video,
A video content related information acquisition unit, wherein the video content related information acquisition means acquires content related information, which is text information describing the content of the video content, from a website;
A text information collecting step for collecting text information about the content from the website based on the word extracted from the content related information and the content creation date or the content release date, and storing the text information in the storage unit;
A topic extraction step for extracting a plurality of words representing a topic as a word set from the text information stored in the storage means;
Image collection / visual pattern generation means collects web images related to the word set, generates a visual pattern related to the topic using the collected web images as learning data, and uses the word set and the visual pattern as an image dictionary. Image collection / visual pattern generation step stored in dictionary storage means;
The image dictionary generation method characterized by performing. In the topic extraction step,
In accordance with the video genre extracted from the content-related information, a video attribute table defining the type of topic to be extracted is stored in the storage means, and each attribute n (n = 1, 2,..., N) of the attribute table is stored. ), The text information stored in the storage means by the text information collection step is read and classified,
The image dictionary generation method according to claim 1, wherein a word set representing a topic related to the attribute n is extracted from the classified text information of the same group. An image dictionary generation device that generates an image dictionary that defines a relationship between a semantic label and a visual pattern for giving a semantic level to a video,
Video content related information acquisition means for acquiring content related information, which is text information describing the content of the video content, from a website;
Text information collecting means for collecting text information about the content from the website based on the word extracted from the content related information and the content creation date or the content release date, and storing the text information in a storage means;
Topic extraction means for extracting a plurality of words representing a topic from the text information stored in the storage means as a word set;
Collecting a web image related to the word set, generating a visual pattern related to the topic using the collected web image as learning data, and storing the word set and the visual pattern as an image dictionary in an image dictionary storage unit Visual pattern generation means;
An image dictionary generation device characterized by comprising: According to the video genre extracted from the content-related information, the video attribute storage means further stores a video attribute table that defines the type of topic to be extracted,
The topic extraction means includes
For each attribute n (n = 1, 2,..., N) in the attribute table of the video attribute storage means, the text information stored in the storage means is read and classified by the text information collection means. Means,
Means for extracting a word set representing a topic related to the attribute n from the classified text information of the same group;
The image dictionary generation device according to claim 3, comprising: On the computer,
A program for realizing the function according to claim 3 or 4.

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