ABSTRACT
This chapter serves as an example to investigate content based image database mining and retrieval, focusing on developing a classification-oriented methodology to address semantics-intensive image retrieval. In this specific approach, with Self Organization Map (SOM) 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 an 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 existing and overlapping among the repositories in the feature space. An algorithm using classification accuracy measures is developed to combine the built classification tree with the fuzzy set modeling method to deliver semantically relevant image retrieval for a given query image. The experimental evaluations have demonstrated that the proposed approach models the semantic relationships effectively and outperforms a state-of-the-art content based image mining system in the literature in both effectiveness and efficiency.
