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Published on March 3, 2008

Author: Bruno

Source: authorstream.com

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Using HTML Metadata to Retrieve Relevant Images from the World Wide Web:  Using HTML Metadata to Retrieve Relevant Images from the World Wide Web Ethan V. Munson University of Wisconsin-Milwaukee Why is image search important?:  Why is image search important? The Web is becoming the world’s primary information source Images are one of the Web’s key features Few WWW image search engines exist currently Using textual search engines to find images manually is laborious A Requirement for Web Image Search:  A Requirement for Web Image Search We need an efficient method of discovering and indexing image content. Two main sources of information about image content: image processing associated text text content markup Related work :  Related work QBIC (the IBM Almaden Research Center) indexes and retrieves images according to: shape color texture object layout queries are formulated through visual examples a sample image user provided sketches Related work QBIC system:  Related work QBIC system Related work QBIC system:  Related work QBIC system Related work QBIC system:  Related work QBIC system QBIC: Advantages and Disadvantages:  QBIC: Advantages and Disadvantages Advantages well-developed visual query language interesting GUI queries are based on image appearance Disadvantages works only at the primitive feature level (color, texture, shape) doesn’t recognize semantics of image very sensitive to camera viewpoint doesn’t scale up to the Web Related work:  Related work WebSeek (J. Smith & S. Chang, Columbia University) performs a semi-automated classification of the images automatically extracts keywords from image file names computes the keyword histogram manually creates a subject hierarchy manually maps the images into the subject hierarchy User can browse the categories search the categories by keyword search the database using image features color content Webseek: Advantages/Disadvantages:  Webseek: Advantages/Disadvantages Advantages Large index of Web images Supports both text and image search Disadvantages Not clear that database can scale up Manual categorization is very expensive Relevance feedback mechanism is computationally expensive Related work:  Related work WebSeer (M. Swain et al., The University of Chicago) uses associated text and markup to supplement information derived from analyzing image content uses multiple kinds of metadata image file names alternate text text of a hyperlink decides which images are photographs, portraits, or computer generated drawing research emphasized categorization, not metadata-based search Why seek new image retrieval methods?:  Why seek new image retrieval methods? The number of WWW documents is growing rapidly and constantly changing We need fast and efficient methods for finding images Image processing is complex computationally expensive limited (misses true image semantics) unnecessary Research Goals:  Research Goals Show that images can be found using HTML “metadata” textual content HTML tag structure attribute values Determine which metadata features are the best clues to image content The URL Filter:  The URL Filter assembles a list of URLs from the results returned by Alta Vista parses the first page returned by Alta Vista follows the URLs of results pages, retrieves these pages, and parses them extracts list of URLs from the results pages The Crawler:  The Crawler retrieves the pages saves each page’s HTML source code in a separate file “Tidy”:  “Tidy” converts arbitrary and probably ill-formed HTML into XHTML XHTML Parser:  XHTML Parser parses an XHTML document builds an XHTML parse tree The Document Analyzer:  The Document Analyzer scans the parse tree for image URLs an image URL appears in either an image or anchor element converts relative URLs into absolute URLs uses various heuristics to determine which URLs point to relevant images Search Strategies :  Search Strategies Image’s file name Textual content of the TITLE element Value of the ALT attribute of IMG elements Textual content of anchor elements Value of the title attribute of anchor elements Textual content of the paragraph surrounding an image Textual content of any paragraph located within the same center element as the image Textual content of heading elements Image Retrieval Experiment:  Image Retrieval Experiment Experimental Questions:  Experimental Questions Which HTML features reveal the most information about image? Do particular patterns of HTML structure carry useful information? Do image search results depend on the type of query? Informal Experiments:  Informal Experiments Conducted extensive informal testing to check software correctness to investigate possible metadata clues to determine rules for filtering out images based on size images smaller than 65 pixels in either dimension almost never contained useful content reduced the number of images we had to classify Metadata Clues:  Metadata Clues Image’s file name Textual content of the TITLE element Value of the ALT attribute of IMG elements Textual content of anchor elements Value of the title attribute of anchor elements Textual content of the paragraph surrounding an image Textual content of any paragraph located within the same center element as the image Textual content of heading elements Query Categories:  Query Categories Famous people “Gorbachev”, “Yeltsin”, and “Streisand” Non-famous people “Yelena” and “Ekaterina” Famous places “Paris” and “London” Less-famous places “Bremen” and “Spokane” Phenomena “Explosion”, “Sunset”, and “Hurricane” Experimental Procedure:  Experimental Procedure For each of the 12 queries Alta Vista returned 200 URLs (20 groups of 10) We used first, middle, and last groups (30 URLs) Downloaded pages and all images on pages excluding small images (< 65 pixels in either dimension) 276 pages and 1578 images were accessible Manually determined relevance of each image Used our system to determine the effectiveness of each of the 8 metadata clue standard information retrieval measures: precision and recall Information Retrieval Measures:  Information Retrieval Measures Recall = B/(A + B) Warning: our study does not really test recall We need a controlled sample of the Web, but instead, we are using Alta Vista’s biased sample Precision = B/(B + D) Relevant, not retrieved A Relevant, retrieved B Nonrelevant, not retrieved C Nonrelevant, retrieved D Recall Table:  Recall Table Precision Table:  Precision Table Key Results:  Key Results Image file name has poor recall for people’s names and excellent recall for less-famous cities Famous names have poorer precision than non-famous and place names Image file name Textual content of TITLE Value of ALT Overall percent of recall Overall percent of precision 43.5 % 62.1 % 13.7 % 70.7 % 58.2 % 87.5 % Problems with this study:  Problems with this study This is a single, small study results must be replicated No standard corpus for testing Web image search our “recall” results are not reliable or truly sound Our choice of tools may bias our results Title tag may be important only because Alta Vista considers it important Tidy may remove some clues What is the structure of “<P> Text <IMG>”? Analysis of “header” clue is questionable Slide60:  Body Body P IMG P IMG Conclusion:  Conclusion Existing content-based image retrieval systems are not good models for Web image search HTML metadata is useful for Web image search Image file name and document title are most useful Alternate text is extremely precise, when present HTML metadata should provide faster image search than image processing approaches no need to download and analyze images can take advantage of existing search engines Using HTML Metadata to Retrieve Relevant Images from the Web:  Using HTML Metadata to Retrieve Relevant Images from the Web Ethan V. Munson Dept. of Electrical Engineering & Computer Science University of Wisconsin - Milwaukee munson@cs.uwm.edu http://www.cs.uwm.edu/~multimedia

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