<Emphasis Type="Italic">I-Quest</Emphasis>: an <Emphasis Type="Italic">i</Emphasis>ntelligent <Emphasis Type="Italic">que</Emphasis>ry <Emphasis Type="Italic">st</Emphasis>ructuring based on user browsing feedback for semantic retrieval of video data |
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Authors: | Tarun?Yadav Email author" target="_blank">Ramazan?Sava??AygünEmail author |
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Affiliation: | (1) Broadband Enterprises, New York, NY, USA;(2) Computer Science Department, Technology Hall N360, University of Alabama in Huntsville, Huntsville, AL 35899, USA |
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Abstract: | In spite of significant improvements in video data retrieval, a system has not yet been developed that can adequately respond
to a user’s query. Typically, the user has to refine the query many times and view query results until eventually the expected
videos are retrieved from the database. The complexity of video data and questionable query structuring by the user aggravates
the retrieval process. Most previous research in this area has focused on retrieval based on low-level features. Managing
imprecise queries using semantic (high-level) content is no easier than queries based on low-level features due to the absence
of a proper continuous distance function. We provide a method to help users search for clips and videos of interest in video
databases. The video clips are classified as interesting and uninteresting based on user browsing. The attribute values of clips are classified by commonality, presence, and frequency within each
of the two groups to be used in computing the relevance of each clip to the user’s query. In this paper, we provide an intelligent
query structuring system, called I-Quest, to rank clips based on user browsing feedback, where a template generation from the set of interesting and uninteresting
sets is impossible or yields poor results.
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Keywords: | Multimedia information retrieval Semantic retrieval Relevance feedback User browsing |
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