This paper demonstrates an approach to image retrieval founded on classifying image regions hierarchically based on their semantics (e.g. sky, snow, rocks, etc.) that resemble peoples' perception rather than on low-level features (e.g, color, texture, shape, etc.). Particularly, we consider outdoor images and automatically classify their regions based on their semantics using the support vector machines (SVMs) tool First, image regions are segmented using the hill-climbing method. Then, those regions are classified by the SVMs. The SVMs learns the semantics of specified classes from a test database of image regions. Such semantic classification allows the implementation of intuitive query interface. As we show in our experiments, the high precision of semantic classification justifies the feasibility of our approach.
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