Content-based image retrieval using the heterogeneous features

The effectiveness of the content-based image retrieval can be enhanced using the heterogeneous features embedded in the images. However, since the features in color and shape are generated using different computation methods and thus may require different similarity measurements, the integration of the retrieval on heterogeneous features is a non-trivial task. In this paper, present a semantics-based clustering approach, to support visual queries on heterogeneous features of images. Using this approach, the database images are classified based on their heterogeneous features. Each semantic image cluster contains a set of sub clusters, which are represented by the heterogeneous features that the images contain. A database image is included into a feature sub cluster, only if, the image contains all the features under the same cluster. A visual query processing strategy is then presented, to support visual queries, on heterogeneous features.