This work is about content based image database retrieval, focusing on developing a classification based methodology to address semantics-intensive image retrieval. With self organization map based image feature grouping, a visual dictionary is created for color, texture, and shape feature attributes, respectively. Labeling each training image with the keywords in the visual dictionary, a classification tree is built. Based on the statistical properties of the feature space we define a structure, called α-semantics graph, to discover the hidden semantic relationships among the semantic repositories embodied in the image database. With the α-semantics graph, each semantic repository is modeled as a unique fuzzy set to explicitly address the semantic uncertainty and the semantic overlap existing among the repositories in the feature space. A retrieval algorithm combining the classification tree with the fuzzy set models to deliver semantically relevant image retrieval is provided. The experimental evaluations have demonstrated that the proposed approach models the semantic relationships effectively and outperforms a state-of-the-art content based image retrieval system in the literature both in effectiveness and efficiency.
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