Patent Publication Number: US-9430478-B2

Title: Anchor image identification for vertical video search

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. application Ser. No. 13/166,810, now U.S. Pat. No. 8,645,353 B2, filed Jun. 23, 2011 and entitled “Anchor Image Identification for Vertical Video Search,” which is expressly incorporated herein by reference in its entirety. 
    
    
     BACKGROUND 
     Search engines are some of the most frequently utilized sites on the World Wide Web (the “Web”). Search engines typically receive input as one or more search terms, perform a query of a database for Web pages matching the search terms, and return search results as a list of hyperlinks to Web pages from which a user can select one of the hyperlinks to visit a corresponding Web page. 
     Users looking for specific content types can choose to search for only a desired content type directly through a content-specific search engine called a vertical search engine. Vertical search engines provide many benefits over traditional, general-purpose search engines, the most fundamental benefit being the extremely focused results that vertical search engines are able to provide for users. For example, a video search engine may provide functionality for searching video content. 
     Video content often has metadata associated therewith that can be useful to a user in determining which search result to select. This metadata may include, for example, title, description, publication/upload date, and/or length for a particular video. In addition to metadata, users often expect a representative image for each video in the search results. A representative image provides users with a visual indication of content contained within a particular video. 
     Gathering metadata and representative images is typically done manually. This can be time-consuming and costly for the search engine provider. Often times, search engine providers are tasked with finding a video, its metadata, and its representative image, and associating this information together in a database. Aside from the time and cost associated with this practice, extracting metadata and finding representative images in this manner can adversely affect the accuracy and precision of search results ultimately provided to users due, for example, to human error. 
     It is with respect to these and other considerations that the disclosure made herein is presented. 
     SUMMARY 
     Concepts and technologies are described herein for anchor image identification for vertical video search. An anchor image is a representative image that has been selected for display as the representative image for a video. Candidate anchor images are representative images for videos that can be selected for a video but may be considered inappropriate as determined, for example, by one or more filtering rules. 
     In accordance with the concepts and technologies disclosed herein, anchor images and information associated therewith are accumulated during a Web crawling operation. One or more rules are applied to the accumulated candidate anchor images to filter out candidate anchor images that are not appropriate for use as the anchor image for a particular target video. The remaining candidate anchor image is then selected as the anchor image for the particular video. 
     According to one aspect, a computer-implemented method for accumulating candidate anchor images includes performing computer-implemented operations for identifying an image link during a Web crawling operation performed by a Web crawler of a video search application and determining if the image link points to a Web page having a video page pattern. The method further includes disregarding the image link if it is determined that the image link does not point to the Web page and associating the image link with the Web page if it is determined that the image like does point to the Web page. 
     According to another aspect, a computer-implemented method for identifying candidate anchor images includes performing computer-implemented operations for identifying a target image for consideration as an anchor image for a target video page, applying one or more rules regarding anchor image selection to the target image, and based upon the rules, determining if the target image should be selected as the anchor image. If it is determined that the target image should not be selected as the anchor image, the method further includes disregarding the target image. If it is determined that the target image should be selected as the anchor image, the method further includes identifying the target image as the anchor image for the target video page. 
     According to yet another aspect, a computer storage medium has computer readable instructions stored thereupon that, when executed by a computer, cause the computer to identify an image link during a Web crawling operation performed by a Web crawler of a video search application, determine if the image link points to a Web page having a video page pattern, disregard the image link if it is determined that the image link does not point to the Web page, and associate the image link with the Web page if it is determined that the image link does point to the Web page. The instructions can further include instructions, the execution of which cause the computer to apply one or more rules regarding anchor image selection to a target image associated with the image link and, based upon application of the rules, determine if the target image should be selected as the anchor image. If it is determined that the target image should not be selected as the anchor image, the instructions cause the computer to disregard the target image. If it is determined that the target image should be selected as the anchor image, the instructions cause the computer to identify the target image as the anchor image for the target video page. 
     It should be appreciated that the above-described subject matter may be implemented as a computer-controlled apparatus, a computer process, a computing system, or as an article of manufacture such as a computer-readable storage medium. These and various other features will be apparent from a reading of the following Detailed Description and a review of the associated drawings. 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended that this Summary be used to limit the scope of the claimed subject matter. Furthermore, the claimed subject matter is not limited to implementations that solve any or all disadvantages noted in any part of this disclosure. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a system diagram illustrating an exemplary operating environment for the various embodiments disclosed herein. 
         FIG. 2  is a diagram illustrating an exemplary modular overview for various application modules and components thereof that are executed by a video search server for the various embodiments disclosed herein. 
         FIG. 3  is a flow diagram showing aspects of a method for generating page groups, according to an exemplary embodiment. 
         FIG. 4  is a flow diagram showing aspects of a method for deduplicating data contained in Web pages of a selected target group to define a candidate region for feature extraction, according to an exemplary embodiment. 
         FIG. 5  is a flow diagram showing aspects of a method for title extraction, according to an exemplary embodiment. 
         FIG. 6  is a flow diagram showing aspects of a method for publication date extraction, according to an exemplary embodiment. 
         FIG. 7  is a flow diagram showing aspects of a method for view count extraction, according to an exemplary embodiment. 
         FIG. 8  is a flow diagram showing aspects of a method for post-processing after entity extraction, according to an exemplary embodiment. 
         FIG. 9  is a flow diagram showing aspects of a method for accumulating candidate anchor images, according to an exemplary embodiment. 
         FIG. 10  is a diagram illustrating anchor image rule generation, according to an exemplary embodiment. 
         FIG. 11  is a flow diagram showing aspects of a method for filtering and selecting an anchor image from a set of candidate anchor images, according to an exemplary embodiment. 
         FIG. 12  is a computer architecture diagram illustrating an exemplary computer hardware and software architecture for a computing system capable of implementing aspects of the embodiments presented herein. 
     
    
    
     DETAILED DESCRIPTION 
     The following detailed description is directed to concepts and technologies for anchor image identification for video search. In accordance with the concepts and technologies disclosed herein, anchor images and information associated therewith are accumulated during a Web crawling operation. One or more rules are applied to the accumulated candidate anchor images to filter out candidate anchor images that are not appropriate for use as the anchor image for a particular target video. The remaining candidate anchor image is then selected as the anchor image for the particular video. 
     While the subject matter described herein is presented in the general context of program modules that execute in conjunction with the execution of an operating system and application programs on a computer system, those skilled in the art will recognize that other implementations may be performed in combination with other types of program modules. Generally, program modules include routines, programs, components, data structures, and other types of structures that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the subject matter described herein may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like. 
     In the following detailed description, references are made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments or examples. Referring now to the drawings, in which like numerals represent like elements throughout the several figures, aspects of a computing system, computer-readable storage medium, and computer-implemented methodology for anchor image identification for video search will be presented. 
     Referring now to  FIG. 1 , aspects of one operating environment  100  for the various embodiments presented herein will be described. The operating environment  100  shown in  FIG. 1  includes a video search server  102  operating on or in communication with a network  104  to provide video search functions to a client device  106 . The video search server  102  is configured to execute an operating system (best illustrated in  FIG. 12 ) and one or more application programs, such as a video search application  108  and/or other application programs. The video search application  108 , in some embodiments, includes a video search engine with components for performing operations such as Web crawling, indexing, and searching for video content. 
     In some embodiments, the video search application  108  includes one or more Web crawlers (also called “Web spiders”) configured to search or “crawl” the Web to obtain data pertinent to video search, including, for example, metadata and one or more still images that are representative of a given video (i.e., representative image), according to the various embodiments disclosed herein. In some embodiments, the video search application  108  is configured to index results obtained by the one or more Web crawlers to generate a video search index  110  and an anchor image storage  112 . In some embodiments, the video search application  108  includes one or more Web pages containing user interface elements for facilitating user input of search query terms via the client device  106 . The video search application  108  may also include other modules and components thereof, as will be described in greater detail below with reference to  FIG. 2 . 
     In the illustrated embodiment, the video search server  102  includes the video search application  108 , the video search index  110 , the anchor image storage  112 , and a video entity template storage  114 . The video search index  110  is configured to store a corpus of uniform resource locators (“URLs”) for video pages in association with metadata extracted via various embodiments disclosed herein and anchor images identified via various embodiments disclosed herein. 
     Exemplary metadata includes, but is not limited to, title, description, publication or upload date, user view count, username of account associated with the upload of a given video, number of comments, number of like or dislike votes for a given video (e.g., thumbs up or thumbs down, etc.), video rating (e.g., five-star scale, 1-10 scale, proprietary scale, etc.), and other user-provided data. Individual metadata types are referred to herein as “entities.” For example, a title is one type of entity, as is a publication date and a view count. These entities can be targets for extraction via the entity extraction methods disclosed herein. 
     The anchor image storage  112  is configured to store candidate anchor images from which an anchor image can be selected in accordance with various embodiments disclosed herein. Anchor images are images that have been selected for display as the representative image for a video. Candidate anchor images are representative images that can be selected for a video. 
     The video entity template storage  114  is configured to store video entity templates generated in accordance with various embodiments disclosed herein. Video entity templates define common features that relate to various entities shared among a group of video Web pages. In some embodiments, each of the Web pages in the group is associated with a single Web site, such as a video hosting Web site. In other embodiments, one or more of the Web pages in the group is associated with a different Web site. Common features defined by a video entity template may include, but are not limited to, hypertext markup language (“HTML”) features, page layout features (e.g., size and/or position of one or more page elements), term use that is indicative of a particular entity (e.g., terms such as “view count” or “upload date” may be indicative of such entities), in any combination thereof, and the like. Additional details of the component of the video search server  102  will become apparent from the balance of this disclosure. 
     Although the video search application  108 , the video search index  110 , the anchor image storage  112 , and the video entity template storage  114  are illustrated as components of the video search server  102 , it should be understood that each of these components, or combinations thereof, may be embodied as or in stand-alone devices or components thereof operating on or in communication with the network  104  and/or the video search server  102 . Thus, the illustrated embodiment is exemplary, and should not be construed as being limiting in any way. 
     The video search server  102  is configured to communicate with the client device  106  over the network  104  to receive a video search query  116 . In response to receiving the video search query  116 , the video search application  108  is configured to process the video search query  116  by parsing the one or more search terms taking into consideration any Boolean operators used to define the relationship between the search terms, compare the search terms to the video search index  110 , and return a video search query result  118  including metadata and a representative image for one or more videos that are relevant to the search terms. The representative image(s) are anchor images selected for the one or more videos. 
     In some embodiments, a representative image included in the video search query result  118  identifies a video page that is host to the video associated with the metadata and representative image using a URL. In some embodiments, the video search result  118  is ordered by relevance to the search terms. In some embodiments, the video search result  118  is grouped according to categories or categories and one or more sub-categories. The video search result  118  can be displayed via a Web browser application  120  of the client device  106 . 
     According to various embodiments, the client device  106  is a personal computer (“PC”) such as a desktop, tablet, or laptop computer system. The client device  106  may include other types of computing systems including, but not limited to, server computers, handheld computers, netbook computers, embedded computer systems, personal digital assistants, mobile telephones, smart phones, or other computing devices. 
     The client device  106  includes the Web browser application  120 , a video player application  122 , a video codec  124 , and an operating system  126 . The Web browser application  102  is configured to request, receive, and display information pages, such as Web pages, from the video search server  102 . In particular, the browser  120  is configured to establish a connection with the video search application  108  executing on the video search server  102 . Through the connection, the browser  120  may request a Web page for executing the video search query  116  provided by the video search application  108 . Through the Web page, a user of the client device  106  can specify one or more search terms to be utilized by the video search application  108  in a search of the video search index  110 . 
     The video player application  122 , in some embodiments, is a plug-in or add-on component to the browser  120  that allows videos to be played in an embedded video player displayed within the browser  120 . In other embodiments, the video player application  122  is a stand-alone application that allows videos to be played in a video player executed separately from the browser  120 . In any case, the video player application  122  is configured to play videos found in a video search on the client device  106  in accordance with one or more video codecs  124 . 
     The operating system  126  is a computer program for controlling the operation of the client device  106 . The browser application  120  and the video player application  122  are executable programs configured to execute on top of the operating system  126  to provide the functionality described above. 
       FIG. 1  illustrates one video search application  102 , one network  104 , one client device  106 , one video search query  116 , and one video search query result  118 . It should be understood, however, that some implementations of the operating environment  100  include multiple video search applications  102 , multiple networks  104 , multiple client devices  106 , multiple video search queries  116 , and/or multiple video search query results  118 . Thus, the illustrated embodiments should be understood as being exemplary, and should not be construed as being limiting in any way. 
     Turning now to  FIG. 2 , a modular overview  200  of various operations performed by the video search application  108  to facilitate extraction of metadata and identification of anchor images within Web pages will be described. In particular, the illustrated modular overview  200  includes three modules  202 ,  204 ,  206  directed, respectively, to various operations, the execution of which facilitate page group generation, template generation, and an anchor image accumulation for the video search application  108 . The page group generation module  202  will now be described. 
     The illustrated page group generation module  202  includes a plurality of Web pages  208 A- 208 D, which may be associated with one or more Web sites. For ease of explanation, consider the pages  208 A- 208 D as being associated with a single target Web site, for example, a video hosting Web site such as YOUTUBE, VIMEO, or the like, from which the video search application  108  is instructed to extract metadata. The target Web site, in some embodiments, is provided to the video search application  108  via a direct user input (e.g., via an input interface of the video search server  102  as best illustrated in  FIG. 12 ). In some embodiments, the target Web site is provided to the video search application  108  via one or more URL seeds of a target URL list, for example, to the Web crawler component of the video search application  108 . In some embodiments, the target Web site is provided to the video search application  108  based upon a target video page pattern. A video page pattern includes, for example, an arrangement of a video Web page. A target video page pattern may be a particular video page pattern that is known to be used by one or more target Web sites. 
     The pages  208 A- 208 D are provided as input to a page group generation component  210 . The page group generation component  210  groups the pages  208 A- 208 D into one or more groups according to common visual layout. For instance, suppose pages  208 A and  208 B include a video section, a comments section, and a related videos section, and a view count section arranged in the same way. Since pages  208 A and  208 B are arranged in this manner, these pages are considered to share a common visual layout and are accordingly grouped together. 
     In the illustrated embodiments, the pages  208 A- 208 D are grouped into three groups  212 A- 212 C in this manner, although more or less groups are contemplated. Assuming the page group generation component  210  groups the pages  208 A- 208 D into two or more groups, a target group selection component  214  selects a one of the groups  208 A- 208 D as the target (i.e., the target group) from which to generate a video entity template. In some embodiments, the target group is selected by leveraging video page classification technology, as those skilled in the art would understand. In the illustrated embodiment, the target group selection component  214  has selected group 1  212 A as the target group. After the target group selection component  214  selects a target group, the target group is provided as input to the template generation module  204 , which is now described in detail. 
     The template generation module  204  is generally configured to generate a video entity template. A video entity template defines common features that relate to various metadata types (also referred to herein as entities) shared among a group of video Web pages. The group of video Web pages may belong to one or more Web sites. Common features may include, for example, hypertext markup language HTML features, term use that is indicative of a particular metadata type, and/or page layout features. The features that are considered to be associated with various entities are described herein below. 
     The illustrated template generation module  204  includes a duplicate element removal component  216  and a repeat region removal component  218  that are configured to remove unnecessary and/or noisy areas of pages within the target group that are likely not to contain metadata. The duplicate element removal component  216  and the repeat region removal component  218  may be executed sequentially as illustrated, sequentially with the repeat region removal component  218  being executed first, or simultaneously. Furthermore, these components may be combined. 
     The duplicate element removal component  216  removes elements that are shared among pages in the target group. These elements are referred to herein as common elements. Common elements may include, for example, page headers, page footers, navigation bars, and the like. The repeat region removal component  218  removes repeat regions of pages in the target group. Repeat regions are portions of pages that are repeated within pages in the target group. Repeat regions may include, but are not limited to, related video sections, comments sections, and advertising regions. The removal of repeat regions further isolates metadata under an assumption that such data cannot or typically would not be presented in such regions of a video page. 
     After the duplicate element removal component  216  and the repeat region removal component  218  are executed, the resultant region is provided as input to a candidate feature extraction component  220 . The resultant region is considered a candidate region in which one or more features reside. The one or more features contained within a candidate region are referred to herein as candidate features. Candidate features may vary based upon the target entity for extraction, as will be described herein. 
     It should be understood that region removal, in some embodiments, is particular to the target entity for extraction. Publication dates and view counts, for example, typically exist in small repeat regions in close proximity to other entities such as title. As a result, these repeat regions may be considered exceptions by the components  216 ,  218 . These exceptions are described in greater detail below with respect to the flow diagrams illustrated in  FIGS. 6 and 7  for publication date and view count entity extraction. 
     The candidate feature extraction component  220  is configured to extract candidate features from the candidate region based upon one or more attributes of a given target entity. A title entity (i.e., the title of a video), for example, may be the same as the title for a given page, or may contain a common prefix or suffix among pages in the target group. This “feature” can be used as an indicator to identify a title entity from HTML content of a given page. Other HTML features such as &lt;H 1 &gt;, &lt;strong&gt;, and the like may be used to identify a title entity in the HTML content. A publication date also has some specific features including a particular date pattern such as “Month.Day.Year,” “Month/Day/Year,” “Month Day Year,” variations thereof with the “Day” or “Year” leading, and the like. These features can be extracted by a predefined date format extractor that is configured to search for dates within pages that are displayed in accordance with one or more date patterns. Other pages may not have a particular publication date or may utilize another format such as “Date Added: X hours/days ago.” In some embodiments, the date format extractor is configured to search for alternative conventions that effectively identify a time at which a particular video was uploaded or published to a host. Methods for title entity extraction, publication date entity extraction, and view count entity extraction are illustrated and described, respectively, herein below with reference to  FIGS. 5-7 . 
     In the illustrated embodiment, candidate features extracted by the candidate feature extraction component  220  are provided to a training component  222  and/or an entity recognition component  224 . The training component  222  uses a machine learning approach to learn the candidate features against a training data set in accordance with a machine learning model  226 , for example, a support vector machine (“SVM”) or other machine learning method known to those skilled in the art. 
     In some embodiments, the entity recognition component  224  is configured to utilize the machine learning model  226  to make a final determination regarding which entity corresponds to which candidate feature of the extracted candidate features. In some embodiments, the final determination made by the entity recognition component  224  is validated via a post processing component  228 . The post processing component  228  is configured to cross-validate the final determination by checking the results within one or more page groups to verify that the target entities are valid across pages within the target group. If the cross-validation fails, the post processing component  228  may request re-recognition by the entity recognition component  220 . Otherwise, if the cross-validation is successful, the final output of the template generation module  204  is a video entity template  230  including which features (e.g., HTML features) correspond to which target entities for pages within the target group. A method for post-processing is illustrated and described herein below with reference to  FIG. 8 . 
     In the illustrated embodiment, the video entity template  230  is provided to a template matching component  232 . In some embodiments, the template matching component  232  receives a target page  234  as input and matches an appropriate template (e.g., the video entity template  230  or another template previously generated) thereto to determine metadata  236  of the target page  234 . The metadata  236  can then be provided to the video search index  110  for use in populating search results in response to a search query (e.g., the search query  116 ) received by the video search application  108 . 
     Turning now to the anchor image accumulation module  206 , the anchor image accumulation module  206  includes components for executing operations related to the accumulation of anchor image candidates for a given video. Specifically, an anchor image discovery component  238  may be executed to locate an image link during a Web crawling operation. The anchor image discovery component  238  checks if an image link found during the Web crawling operation points to (i.e., links to) a video Web page. In other words, the anchor image discovery component  238  checks if an image link found during the Web crawling operation points to a Web page having a video page pattern associated therewith. If the image link points to a video page pattern, the image link is associated with a target video page (e.g., the target page  234 ). For example, if image A contains a link to page B, image A is stored in association with page B in the anchor image storage  112 . An anchor image selection component  240  then applies one or more rules to determine whether a particular image can be selected. The resultant image is an anchor image  242  for the target page  234  and is provided to the video search index  110 . Methods for accumulating candidate anchor images, generating anchor image rules, and filtering candidate anchor images for selection based upon the rules are described herein below with respect to  FIGS. 9-11 . 
     Turning now to  FIG. 3 , aspects of a method  300  for generating page groups will be described in detail. It should be understood that the operations of the methods disclosed herein are not necessarily presented in any particular order and that performance of some or all of the operations in an alternative order(s) is possible and is contemplated. The operations have been presented in the demonstrated order for ease of description and illustration. Operations may be added, omitted, and/or performed simultaneously, without departing from the scope of the appended claims. 
     It also should be understood that the illustrated methods can be ended at any time and need not be performed in their respective entireties. Some or all operations of the methods, and/or substantially equivalent operations, can be performed by execution of computer-readable instructions included on a computer-storage media, as defined below. The term “computer-readable instructions,” and variants thereof, as used in the description and claims, is used expansively herein to include routines, applications, application modules, program modules, programs, components, data structures, algorithms, and the like. Computer-readable instructions can be implemented on various system configurations, including single-processor or multiprocessor systems, minicomputers, mainframe computers, personal computers, hand-held computing devices, processor-based, programmable consumer electronics, combinations thereof, and the like. 
     Thus, it should be appreciated that the logical operations described herein are implemented (1) as a sequence of computer-implemented acts or program modules running on a computing system and/or (2) as interconnected machine logic circuits or circuit modules within the computing system. The implementation is a matter of choice dependent on the performance and other requirements of the computing system. Accordingly, the logical operations described herein are referred to variously as states, operations, structural devices, acts, or modules. These operations, structural devices, acts, and modules may be implemented in software, in firmware, in special purpose digital logic, and any combination thereof. 
     For purposes of illustrating and describing the concepts of the present disclosure, the methods disclosed herein are described as being performed by the video search application  108  executing on the video search server  102  and, in some instances, more particularly, the application modules  202 ,  204 ,  206  or specific components thereof illustrated and described above with reference to  FIG. 2 . It should be understood that these embodiments are exemplary, and should not be viewed as being limiting in any way. The method  300  is now described with reference to  FIG. 2  for further ease of description and illustration. The method  300  includes operations performed by the page group generation module  202  of the video search application  108  upon being executed by the video search server  102 . 
     The method  300  begins and proceeds to operation  302 , wherein a target Web site including a plurality of Web pages (e.g., the pages  208 A- 208 D) is analyzed to determine which of the plurality of Web pages share a common visual layout. From operation  302 , the method  300  proceeds to operation  304 , wherein the Web pages are grouped based upon common visual layout. For example, for a given video hosting Web site (e.g., YOUTUBE), the Web pages associated with that Web site can be analyzed for common visual layout wherein regions such as header, footer, navigation bar, video placeholder, comments region, ratings region, related video region, and the like are shared among pages that contain video within the Web site. 
     It should be understood that some Web sites may have Web pages that share a common URL pattern, but have a different visual layout. Thus, grouping the pages based upon common visual layout instead of common URL pattern filters potentially unrelated pages from the groups prior to further filtering, post group page generation operations. From operation  304 , the method  300  proceeds to operation  306 , wherein a target group is selected. From operation  306 , the method  300  proceeds to operation  308 . The method  300  ends at operation  308 . 
     Turning now to  FIG. 4 , aspects of a method  400  for deduplicating data contained in the Web pages of the selected target group will be described with reference to  FIG. 2  for further ease of description and illustration. The method  400  includes operations performed by the template generation module  204  of the video search application  108  upon being executed by the video search server  102 . More particularly, the method  400  includes operations performed by the duplicate element removal component  216  and/or the repeat region removal component  218 . 
     The method  400  begins and proceeds to operation  402 , wherein common page elements shared among Web pages in the target group are removed. Common elements such as header, footer, navigation bar, and the like can be removed at operation  402 . From operation  402 , the method  400  proceeds to operation  404 , wherein common page elements that are contained within one or more repeated regions of the Web pages in the target group are removed. The resultant pages include a candidate region containing page elements that are considered likely to be associated with videos. From operation  404 , the method  400  proceeds to operation  406 . The method  400  ends at operation  406 . 
     Turning now to  FIG. 5 , aspects of a method  500  for title entity extraction within a candidate region will be described with reference to  FIG. 2  for further ease of description and illustration. The method  500  includes operations performed by the template generation module  204  of the video search application  108  upon being executed by the video search server  102 . More particularly, the method  500  includes operations performed by the candidate feature extraction component  220 , the training component  222 , the entity recognition component  224 , and/or the machine learning model  226 . 
     In some video pages, the HTML page title is also the video title or it contains the video title together with some common prefix or suffix. This attribute of video titles can be used to extract the page title entity for a given page as a candidate feature. Titles in video pages also likely have some strong HTML features, such as &lt;H 1 &gt;, &lt;strong&gt;, and/or the like. These attributes of video titles can additionally or alternatively be used to identify and extract candidate features for titles. In some embodiments, the HTML page title is extracted for a given page within the target group and is compared to the HTML page titles of other pages within the target group to filter any common prefix or suffix shared among the HTML page titles. The resultant filtered title can then be identified as the real page title and a true candidate feature. If the HTML page titles are the same, however, the HTML titles may be extracted and provided to the machine learning model  226  for consideration as part of a machine learning data set. 
     The method  500  begins and proceeds to operation  502 , wherein cross-page deduplication is performed on the pages in the target group to isolate candidate features. From operation  502 , the method  500  proceeds to operation  504 , wherein extraction of the HTML page title is attempted. From operation  504 , the method  500  proceeds to operation  506 , wherein it is determined if extraction of the HTML title is successful. If extraction of the HTML page title is successful, the method  500  proceeds from operation  506  to operation  508 , wherein cross-page filtering of prefix and/or suffix from the extracted HTML page title is performed to isolate a real page title sans any prefix and/or suffix share among pages in the target group. 
     From operation  508 , the method  500  proceeds to operation  510 , wherein it is determined if isolating the real page title is successful. Isolating the real page title is successful if the extracted HTML titles have common prefix or common suffix. If isolating the real page title is successful, the method  500  proceeds from operation  510  to operation  512 , wherein an edit distance between the cross-page operation outputs (i.e., the outputs of operations  502 ,  508 ) is calculated. From operation  512 , the method  500  proceeds to operation  514 , wherein one of the candidate features is selected based at least in part upon the edit distance calculated in operation  512 . From operation  514 , the method  500  proceeds to operation  516 . The method  500  ends at operation  516 . 
     If isolating the real page title is not successful as determined at operation  510  or if the HTML page title is not successfully extracted as determined at operation  506 , the method  500  proceeds to operation  518 , wherein the candidate features are added to a training data set of the machine learning model  226 . From operation  518 , the method  500  proceeds to operation  520 , wherein the candidate features are classified according to the model. In the illustrated embodiment, these candidate features are classified for title entity extraction. From operation  520 , the method  500  proceeds to operation  522 , wherein a cross-page vote is performed on the candidate features. From operation  522 , the method  500  proceeds to operation  524 , wherein the candidate feature with the highest vote (e.g., most common candidate feature among the candidate features in the pages of the target group) is selected and output as the candidate feature for extraction as the title entity within the target group. From operation  524 , the method  500  proceeds to operation  516 . The method  500  ends at operation  516 . 
     Turning now to  FIG. 6 , aspects of a method  600  for publication date extraction within a candidate region will be described with reference to  FIG. 2  for further ease of description and illustration. The method  600  includes operations performed by the template generation module  204  of the video search application  108  upon being executed by the video search server  102 . More particularly, the method  600  includes operations performed by the candidate feature extraction component  220 , the training component  222 , the entity recognition component  224 , and/or the machine learning model  226 . 
     A publication date (or upload date) for a given video may exist in a small repeat region of a video page. Accordingly, the method  600  begins and proceeds to operation  602 , wherein during the repeat region removal operation (e.g., operation  404  of  FIG. 4 ) certain region sizes are treated as exceptions so as not to be removed. These regions may include regions that are below a certain size threshold. It is contemplated that the size threshold can be trained over time and adjusted to include region sizes that have been found to typically include publication dates for videos. In some instances, the region size exception is for region sizes that include the publication date within a threshold distance of the title or other entity of interest. In any case, the method  600  considers repeat regions that are likely to include a publication date. 
     From operation  602 , the method  600  proceeds to operation  604 , wherein candidate features are extracted using a date format extractor. It is contemplated that the date format extractor can target any number of date formats including various formats that start with the day, the month, or the year. From operation  604 , the method  600  proceeds to operation  606 , wherein a distance to the title is calculated for each candidate feature. The distance may be measured in pixels, inches, or some other unit of measurement. 
     From operation  606 , the method  600  proceeds to operation  608 , wherein distances that exceed a distance threshold are filtered such that the candidate features associated with the remaining distances have a higher probability of being the publication date. From operation  608 , the method  600  proceeds to operation  610 , wherein a cross-page vote is performed on the candidate features. 
     From operation  610 , the method  600  proceeds to operation  612 , wherein the candidate feature with the highest vote (e.g., most common candidate feature among the candidate features in the pages of the target group) is selected and output as the candidate feature for extraction as the publication date entity within the target group. From operation  612 , the method  600  proceeds to operation  614 . The method  600  ends at operation  614 . 
     Turning now to  FIG. 7 , aspects of a method  700  for view count extraction within a candidate region will be described with reference to  FIG. 2  for further ease of description and illustration. The method  700  includes operations performed by the template generation module  204  of the video search application  108  upon being executed by the video search server  102 . More particularly, the method  700  includes operations performed by the candidate feature extraction component  220 , the training component  222 , the entity recognition component  224 , and/or the machine learning model  226 . 
     View count is a number, which is very similar to other numbers typically found in a video page, such as number of votes, number of users that have identified the video as a favorite, the number of comments, and the like. For this reason it is historically difficult to distinguish the view count of a given video from other numerical metadata associated with that video. The method  700  for view count extraction utilizes a white list of terms that are typically associated with a view count. This white list may be updated from time to time to include new terms. Furthermore, although a white list is described with particular reference to view counts, other numerical entities can be extracted using a similar methodology. 
     The method  700  begins and proceeds to operation  702 , wherein during the repeat region removal operation (e.g., operation  404  of  FIG. 4 ) certain region sizes are treated as exceptions so as not to be removed. These regions may include regions that are below a certain size threshold. It is contemplated that the size threshold can be trained over time and adjusted to include region sizes that have been found to typically include view counts for videos. In some instances, the region sizes exception is for region sizes that include the view count within a threshold distance of the title or other entity of interest. In any case, the method  700  considers repeat regions that are likely to include a view count. 
     From operation  702 , the method  700  proceeds to operation  704 , wherein candidate features are extracted. In some embodiments, the candidate features are extracted utilizing a digital format extractor. The digital format extractor is utilized to extract features such as video count candidates by recognizing a common view count format. Some examples of view count formats include, but art not limited to, “ 123456 ”, “ 123 , 456 ”, and “( 123456 )”. The candidate features may then be filtered by a white list. 
     From operation  704 , the method  700  proceeds to operation  706 , wherein a distance to the title is calculated for each candidate feature. The distances may be measured in pixels, inches, or some other unit of measurement. From operation  706 , the method  700  proceeds to operation  708 , wherein distances that exceed a distance threshold are filtered such that the candidate features associated with the remaining distances have a higher probability of being the view count. From operation  708 , the method  700  proceeds to operation  710 , wherein a cross-page vote is performed on the candidate features. From operation  710 , the method  700  proceeds to operation  712 , wherein the candidate feature with the highest vote (e.g., most common candidate feature among the candidate features in the pages of the target group) is selected and output as the candidate feature for extraction as the view count entity within the target group. From operation  712 , the method  700  proceeds to operation  714 . The method  700  ends at operation  714 . 
     Turning now to  FIG. 8 , aspects of a method  800  for post-processing after entity extraction will be described with reference to  FIG. 2  for further ease of description and illustration. The method  800  includes operations performed by the template generation module  204  of the video search application  108  upon being executed by the video search server  102 . More particularly, the method  500  includes operations performed by the post processing component  228 . The selected candidate feature provided as output of one of the entity extraction methods  500 ,  600 ,  700  is considered as input to the method  800 . 
     The method  800  begins and proceeds to operation  802 , wherein the selected candidate feature is cross-validated with candidate features of other pages within the target group. From operation  802 , the method  800  proceeds to operation  804 , wherein it is determined if the selected candidate feature is valid as determined through the cross-validation. If it is determined at operation  804  that the selected candidate feature is valid, the method  800  proceeds to operation  806 , wherein a video entity template is generated including an indication of which pages elements correspond to which entities for a given video page in the target Web site. 
     From operation  806 , the method  800  proceeds to operation  808 , wherein the video entity template is output. From operation  808 , the method  800  proceeds to operation  810 , wherein the video entity template is incorporated into the video search index  110  such that future video pages from the target Web site can be analyzed and the metadata corresponding to various entities can be efficiently extracted. From operation  810 , the method  800  proceeds to operation  812 . The method  800  ends at operation  812 . 
     If it is determined at operation  804  that the selected candidate feature is not valid, the method  800  proceeds to operation  814 , wherein the appropriate entity extraction method (e.g., for title entity extraction, method  500 ; for publication date entity extraction, method  600 ; for view count entity extraction, method  700 ) is repeated. Cross-validation in accordance with the method  800  may then be repeated as illustrated. 
     Turning now to  FIG. 9 , aspects of a method  900  for accumulating candidate anchor images will be described. The method  900  includes operations performed by the anchor accumulation module  206  of the video search application  108  upon being executed by the video search server  102 . More particularly, the method  900  includes operations performed by the anchor image discovery component  238 . 
     The method  900  begins and proceeds to operation  902 , wherein an image link is identified during a crawling operation performed by one or more Web crawlers of the video search application  108 . A video list is one possible source for candidate anchor images, so the crawling operation could focus on lists, such as those included within different channels of a video site. Other lists are provided based upon some criteria such as most views (i.e., most popular), most recent, least recent, highest rated, lowest rated, and the like. The crawling operation may also consider these lists. Another source for candidate anchor images is inside video pages such as under a related videos section wherein images and corresponding links are often displayed. In general, at operation  902 , the Web crawlers are instructed to find all images that have a link to a certain target video page. 
     From operation  902 , the method  900  proceeds to operation  904 , wherein it is determined if the image link points to (i.e., links to) a video page pattern. In some embodiments, URLs that are associated with a video page pattern are identified through the page grouping operations performed by the group generation module  202 . In some embodiments, it can be determined if a target URL is a video page or not by using the higher number of like images targeting the same URL as an indication that the probability of image A being associated with page B is very high. Over time, this information will accumulate nearly all the image for target URL. If it is determined that the image link points to a page containing a video page pattern, the image link is associated with the target page (i.e., the linked to page) and stored in the anchor image storage  112  as a candidate anchor image. From operation  906 , the method  900  proceeds to operation  908 . The method  900  ends at operation  908 . If it is determined at operation  904  that the image link does not point to a page containing a video page pattern, the method  900  proceeds to operation  910 , wherein the image link is disregarded. The method  900  then proceeds to operation  908 , wherein the method  900  ends. 
     Turning now to  FIG. 10 , a diagram illustrating anchor image rule generation will be described. As illustrated, from the anchor image storage  112 , anchor image candidates for various sites can be analyzed to perform anchor image rule generations based upon each site. In the illustrated embodiment, sets of anchor image candidates  1000 ,  1002 ,  1004 ,  1006  corresponding, respectively, to sites  1 ,  2 ,  3 , and N are illustrated. By way of example, the set of anchor image candidates  1000  for site  1  is provided as input to an anchor image rule generation module  1008  to generate anchor image rules for site  1 . 
     The illustrated anchor image rule generation module  1008  utilizes various factors to determine a probability of a given anchor image candidate within a set of anchor image candidates set being a good anchor image or a bad anchor image. Exemplary factors are illustrated as including a first factor  1010  of a number of video pages to which a given image points, a second factor  1012  of a size and weight of a given image, a third factor  1014  of whether a URL associated with a given image is explicitly included in or matches a pattern in a black list, and a fourth factor  1016  of whether a URL associated with a given image is explicitly included in or matches a pattern in a white list. The factors  1010 ,  1012 ,  1014 ,  1016  are considered by an anchor image model  1018  to generate one or more anchor image rules  1020 , which may or may not be verified by a rule verification component  1022 . The anchor image rules  1020  are then provided to the anchor image selection component  240  (described above with reference to  FIG. 2 ). Those skilled in the art will appreciate other factors as being used to generate rules. 
     The first factor  1010  considers whether a given image points to too many video pages. This may be determined, for example, by determining a number of video pages to which the given image points and comparing the number to a threshold number of video pages. In some embodiments, the threshold number of video pages is any number as few as two video pages. For example, a threshold number of two may be arrived at under the assumption that two unique videos should have anchor images that uniquely identify each video and, accordingly, should not utilize an image that is shared among two or more video pages as the anchor image. For further example, an image used for a play button, rewind button, fast forward button, stop button, or pause button may be considered a bad anchor image since these types of images (i.e., control buttons) are likely to be shared among a number of video pages. 
     The second factor  1012  considers whether a given image is too small or too large. The determination of what is too small and too large may be based upon size constraints set by the video search server  102 , a search engine provider, another computer system or device, or a third party. For example, size constraints may be set for compatibility with a search interface or for some other reason. If the given image is too small or too large, the given image may be considered a bad image. 
     The second factor  1012  also considers whether the weight of a given image. The weight may be the relative strength, heaviness, or darkness of the given image or a portion (e.g., a line) thereof against a background of the given image. The weight may be similar to those in terms of thinness or thickness of the image. Weight constraints may be set in a manner similar to the size constraints described above. 
     The third factor  1014  is a white list used to identify one or more image URL patterns and/or one or more specific URLs that are allowed. In some embodiments, image URL patterns included in a white list may include image URL patterns that indicate the associated image is hosted by a Web site that hosts videos. 
     The fourth factor  1016  is a black list used to identify one or more image URL patterns and/or one or more specific URLs that are not allowed. In some embodiments, image URL pattern rules included in a black list are prohibited URL patterns, such as URL patterns that indicate the associated image is being hosted by a non-trusted Web site (e.g., a site which may use inappropriate language in URLs or otherwise mislabel image URLs). 
     The rule verification component  1020  is used to verify whether a generated rule is good or not. In some embodiments, the verification component  1020  filters out rules which generate low quality images such as, but not limited to, black screen, gray screen, or color bar. What is considered to be a low quality image may be adjusted by the search engine provider or another party, and may be adjusted per Web site or per group of Web sites. If a rule is good, it is made available to the anchor image selection component  240  for consideration when performing anchor image selection, such as described in greater detail below with reference to  FIG. 11 . 
     Turning now to  FIG. 11 , a flow diagram showing aspects of a method  1100  for filtering and selecting an anchor image from a set of candidate anchor images will be described. The method  1100  begins and proceeds to operation  1102 , wherein a target image is identified for consideration as an anchor image for a target video page. Alternatively, a plurality of target images, each of which is identified as linking to the same target video page, are considered as candidate anchor images for the target video page. In any case, from operation  1102 , the method  1100  proceeds to operation  1104 , wherein one or more rules regarding the selection of an anchor image are applied. 
     Exemplary rules include, but are not limited to, rules related to image dimensions, image URL pattern, and text similarity within image to title of target video. Image dimension rules, in some embodiments, include minimum and/or maximum image dimensions such that candidate anchor images that do not at least meet the minimum image dimensions or those that exceed the maximum image dimensions are excluded from consideration as the target anchor image. 
     In some embodiments, image URL pattern rules include a white list of allowed URL patterns, for example, URL patterns that indicate the associated image being hosted by a Web site also hosting the target video. In other embodiments, image URL pattern rules include a black list of prohibited URL patterns, for example, URL patterns that indicate the associated image is being hosted by a non-trusted Web site (e.g., a site which may use inappropriate language in URLs or otherwise mislabel image URLs). Those skilled in the art will appreciate other rules as being applicable to filter images such that the filtered images are excluded from further consideration as the anchor image. 
     From operation  1104 , the method  1100  proceeds to operation  1106 , wherein it is determined if the target image should be selected as the anchor image. This determination is based at least partially upon the application of one or more rules in operation  1104 . If the application of the rules does not exclude the target image, the method  1100  proceeds to operation  1108 , wherein the target and information associated therewith is associated with the target video page. The target image can also be stored as the anchor image for the target video page. From operation  1108 , the method  1100  proceeds to operation  1110 . The method  1100  ends at operation  1110 . If the application of the rules does exclude the target image, the method  1100  proceeds to operation  1112 , wherein the target image is disregarded. The method  1100  then proceeds to operation  1110 , wherein the method  1100  ends. 
     Turning now to  FIG. 12 , an exemplary computer architecture  1200  for a device capable of executing the software components described herein for data extraction and anchor image identification for video search. Thus, the computer architecture  1200  illustrated in  FIG. 12  illustrates an architecture for a server computer (e.g., the video search server  102 ). The computer architecture  1200  may be utilized to execute any aspects of the software components presented herein, such as those illustrated and described above with respect to  FIGS. 2-11 . 
     The computer architecture  1200  illustrated in  FIG. 12  includes a central processing unit  1202  (“CPU”), a system memory  1204 , including a random access memory  1206  (“RAM”) and a read-only memory (“ROM”)  1208 , and a system bus  1210  that couples the memory  1204  to the CPU  1202 . A basic input/output system containing the basic routines that help to transfer information between elements within the computer architecture  1200 , such as during startup, is stored in the ROM  1208 . The computer architecture  1200  further includes a mass storage device  1212  for storing an operating system  1214 , the video search application  108 , the video search index  110 , the anchor image storage  112 , and the video entity template storage  114 . 
     The mass storage device  1212  is connected to the CPU  1202  through a mass storage controller (not shown) connected to the bus  1210 . The mass storage device  1212  and its associated computer-readable media provide non-volatile storage for the computer architecture  1200 . Although the description of computer-readable media contained herein refers to a mass storage device, such as a hard disk or CD-ROM drive, it should be appreciated by those skilled in the art that computer-readable media can be any available computer storage media or communication media that can be accessed by the computer architecture  1200 . 
     Communication media includes computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics changed or set in a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer-readable media. 
     By way of example, and not limitation, computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, application components, or other data. For example, computer media includes, but is not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid state memory technology, CD-ROM, digital versatile disks (“DVD”), HD-DVD, BLU-RAY, or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computer architecture  1200 . For purposes of the claims, the phrase “computer storage medium” and variations thereof, does not include waves, signals, and/or other transitory and/or intangible communication media, per se. 
     According to various embodiments, the computer architecture  1200  may operate in a networked environment using logical connections to remote computers through a network such as the network  104 . The computer architecture  1200  may connect to the network  104  through a network interface unit  1216  connected to the bus  1210 . It should be appreciated that the network interface unit  1216  also may be utilized to connect to other types of networks and remote computer systems, for example, other video search servers, databases, or data stores configured as redundant backup systems for the video search server  102  or multiples of the video search server  102  configured to provide greater capacity for video search functions. The computer architecture  1200  also may include an input/output controller  1218  for receiving and processing input from a number of other devices, including a keyboard, mouse, or electronic stylus (not shown in  FIG. 12 ). Similarly, the input/output controller  1218  may provide output to a display screen, a printer, or other type of output device (also not shown in  FIG. 12 ). 
     It should be appreciated that the software components described herein may, when loaded into the CPU  1202  and executed, transform the CPU  1202  and the overall computer architecture  1200  from a general-purpose computing system into a special-purpose computing system customized to facilitate the functionality presented herein. The CPU  1202  may be constructed from any number of transistors or other discrete circuit elements, which may individually or collectively assume any number of states. More specifically, the CPU  1202  may operate as a finite-state machine, in response to executable instructions contained within the software modules disclosed herein. These computer-executable instructions may transform the CPU  1202  by specifying how the CPU  1202  transitions between states, thereby transforming the transistors or other discrete hardware elements constituting the CPU  1202 . 
     Encoding the software modules presented herein also may transform the physical structure of the computer-readable media presented herein. The specific transformation of physical structure may depend on various factors, in different implementations of this description. Examples of such factors may include, but are not limited to, the technology used to implement the computer-readable media, whether the computer-readable media is characterized as primary or secondary storage, and the like. For example, if the computer-readable media is implemented as semiconductor-based memory, the software disclosed herein may be encoded on the computer-readable media by transforming the physical state of the semiconductor memory. For example, the software may transform the state of transistors, capacitors, or other discrete circuit elements constituting the semiconductor memory. The software also may transform the physical state of such components in order to store data thereupon. 
     As another example, the computer-readable media disclosed herein may be implemented using magnetic or optical technology. In such implementations, the software presented herein may transform the physical state of magnetic or optical media, when the software is encoded therein. These transformations may include altering the magnetic characteristics of particular locations within given magnetic media. These transformations also may include altering the physical features or characteristics of particular locations within given optical media, to change the optical characteristics of those locations. Other transformations of physical media are possible without departing from the scope and spirit of the present description, with the foregoing examples provided only to facilitate this discussion. 
     In light of the above, it should be appreciated that many types of physical transformations take place in the computer architecture  1200  in order to store and execute the software components presented herein. It also should be appreciated that the computer architecture  1200  may include other types of computing devices, including hand-held computers, embedded computer systems, personal digital assistants, and other types of computing devices known to those skilled in the art. It is also contemplated that the computer architecture  1200  may not include all of the components shown in  FIG. 12 , may include other components that are not explicitly shown in  FIG. 12 , or may utilize an architecture completely different than that shown in  FIG. 12 . 
     Based on the foregoing, it should be appreciated that concepts and technologies for anchor image identification for video search have been disclosed herein. Although the subject matter presented herein has been described in language specific to computer structural features, methodological and transformative acts, specific computing machinery, and computer readable media, it is to be understood that the invention defined in the appended claims is not necessarily limited to the specific features, acts, or media described herein. Rather, the specific features, acts and mediums are disclosed as example forms of implementing the claims. 
     The subject matter described above is provided by way of illustration only and should not be construed as limiting. Various modifications and changes may be made to the subject matter described herein without following the example embodiments and applications illustrated and described, and without departing from the true spirit and scope of the present invention, which is set forth in the following claims.