To improve the retrieval accuracy of content-based image retrieval, an important task is to reduce the 'semantic gap' between low-level image features and the richness of human semantics. In this paper, we present a region-based image retrieval system with high-level semantic concepts used. The contribution of the paper is two-fold. First, salient low-level features are extracted from arbitrary-shaped regions. Second, a fuzzy-ID3 decision tree learning method is proposed to derive association rules which map low-level image features to high-level concepts. Experimental results prove that by reducing the 'semantic gap', the proposed system not only improves the retrieval accuracy, but also supports users in query-by-keyword.
展开▼
