Deriving High-Level Concepts Using Fuzzy-ID3 Decision Tree for Image Retrieval
Ying Liu, Dengsheng Zhang, Guojun Lu, Wei‐Ying Ma · 2006
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. We present a region-based image retrieval system using high-level semantic concepts. The contribution of the paper is two-fold. First, salient low-level features are extracted from arbitrarily-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.