Automatic Matching Tool Selection

نویسندگان

  • Yong Rui
  • Thomas S. Huang
  • Sharad Mehrotra
  • Michael Ortega
چکیده

For a given visual feature, due to the diversity of hu-man's subjective judgment, a visual information retrieval system that supports a single preexed similarity measure will result in poor retrieval performance. To address this problem, this paper proposes the concept of similarity matching toolkit which consists of different similarity measures simulating human's perceptions of the given feature from diierent aspects. The toolkit supports a feedback-driven tool selection mechanism which adapts to the similarity measure that best ts the user's perception. To illustrate the advantage of the proposed toolkit approach, we apply it to shape-based image retrieval. The paper describes a shape matching toolkit consisting of four transformation-invariant and computation-ally eecient matching tools and describes how relevance feedback can be used for automatic tool selection. Experimental results validate the exibility of the matching toolkit and show the eeectiveness of the relevance feedback for shape matching tool selection.

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تاریخ انتشار 1997