Region-based Semantic Image Classification

In the field of content-based image retrieval, there exist a gap between low-level descriptions of image content and the semantic needs of users to query image databases. 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 a support vector machines (SVMs). The SVMs learns the semantics of specified classes from specific low-level feature of the test image regions. Image regions are, first, segmented using a hill-climbing approach. Then, those regions are classified by the SVMs. 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.