Combining Clustering and Relevance Feedback Techniques in Content-based Image Retrieval

O. Marques and B. Furht (USA)


Content-based image search and retrieval, Relevance feedback, Clustering techniques, Multimedia database systems,Digital image processing


This paper presents a summary of results of ongoing research on the impact of combining clustering and rele vance feedback techniques in a content-based image re trieval (CBIR) system. It describes a novel approach to learning the user’s goals and the relevance of each fea ture for a particular search. by employing a combination of Bayesian learning algorithm and cluster analysis tech niques. Results of experiments show that the proposed model exhibits good performance for moderate-size, uncon strained databases, encouraging further research and im provements.

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