Relevance Feedback for Content Based Retrieval Using Fuzzy Integral

Young Sik Choi

Relevance Feedback for Content Based Retrieval Using Fuzzy Integral

Young Sik Choi, Journal of Internet Computing and Services, Vol. 1, No. 2, pp. 89-0, Dec. 2000
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Abstract

Relevance feedback is a technique to learn the user's subjective perception of similarity between images, and has recently gained attention in Content Based Image Retrieval. Most relevance feedback methods assume that the individual features that are used in similarity judgments do not interact with each other. However, this assumption severely limits the types of similarity judgments that can be modeled In this paper, we explore a more sophisticated model for similarity judgments based on fuzzy measures and the Choquet Integral, and propose a suitable algorithm for relevance feedback, Experimental results show that the proposed method is preferable to traditional weighted- average techniques.

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Cite this article

[APA Style]

Choi, Y. (2000). Relevance Feedback for Content Based Retrieval Using Fuzzy Integral. Journal of Internet Computing and Services, 1(2), 89-0.

[IEEE Style]

Y. S. Choi, "Relevance Feedback for Content Based Retrieval Using Fuzzy Integral," Journal of Internet Computing and Services, vol. 1, no. 2, pp. 89-0, 2000.

[ACM Style]

Young Sik Choi. 2000. Relevance Feedback for Content Based Retrieval Using Fuzzy Integral. Journal of Internet Computing and Services, 1, 2, (2000), 89-0.