Abstract
In content-based image retrieval (CBIR), similarity measures vary according to the user, and it is difficult to build a retrieval system which reflects the user's similarity measures automatically. Regarding CBIR as consisting of feature extraction, coarse classification and detailed matching stages, this work aims at reflecting the user's similarity measures in coarse classification. After obtaining the user's evaluation to the initial retrieval, we transform the initial feature vectors using optimal linear associative memory (OLAM). This leads to the selection of important features from the user's relevance feedback. Experimental results show the effectiveness of the proposed method which reflects the user's similarity measures in the coarse classification.
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