{"id":151718,"date":"2004-06-01T00:00:00","date_gmt":"2004-06-01T00:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/msr-research-item\/data-driven-approach-for-bridging-the-cognitive-gap-in-image-retrieval\/"},"modified":"2018-10-16T21:47:11","modified_gmt":"2018-10-17T04:47:11","slug":"data-driven-approach-for-bridging-the-cognitive-gap-in-image-retrieval","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/data-driven-approach-for-bridging-the-cognitive-gap-in-image-retrieval\/","title":{"rendered":"Data-Driven Approach for Bridging the Cognitive Gap in Image Retrieval"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Bridging the cognitive gap in image retrieval has been an active research direction in recent years. Existing solutions typically require a large volume of training data that could be difficult to obtain in practice. In this paper, we propose a data-driven approach that uses Web images and their surrounding textual annotations as the source of training data to bridge the cognitive gap. We construct an image thesaurus that contains a set of codewords, each representing a semantically related subspace in the feature space. We also explore the use of query expansion based on the constructed image thesaurus for improving image retrieval performance.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Bridging the cognitive gap in image retrieval has been an active research direction in recent years. Existing solutions typically require a large volume of training data that could be difficult to obtain in practice. In this paper, we propose a data-driven approach that uses Web images and their surrounding textual annotations as the source of [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"xjwang"},{"type":"user_nicename","value":"wyma"},{"type":"user_nicename","value":"xinl"}],"msr_publishername":"Institute of Electrical and Electronics Engineers, Inc.","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"MSR-TR-2004-53","msr_organization":"","msr_pages_string":"4","msr_page_range_start":"4","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"\u00a9 2004 IEEE. Personal use of this material is permitted. However, permission to reprint\/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.","msr_conference_name":"","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"Microsoft 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