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Deriving High-Level Concepts Using Fuzzy-ID3 Decision Tree for Image Retrieval

By Ying Liu, Dengsheng Zhang, Guojun Lu and Wei-ying Ma

Abstract

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 highlevel 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. 1

Year: 2005
OAI identifier: oai:CiteSeerX.psu:10.1.1.144.7574
Provided by: CiteSeerX
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