The need for efficient content-based image retrieval has increased tremendously in many application areas such as biomedicine, military, commerce, education, and Web image classification and searching. We present here SIMPLIcity (Semanticssensitive Integrated Matching for Picture LIbraries), an image retrieval system, which uses semantics classification methods, a wavelet-based approach for feature extraction, and integrated region matching based upon image segmentation. As in other regionbased retrieval systems, an image is represented by a set of regions, roughly corresponding to objects, which are characterized by color, texture, shape, and location. The system classifies images into semantic categories, such as textured-nontextured, graphphotograph. Potentially, the categorization enhances retrieval by permitting semantically-adaptive searching methods and narrowing down the searching range in a database. A measure for the overall similarity between images is developed using a region-matching scheme that integrates properties of all the regions in the images. Compared with retrieval based on individual regions, the overall similarity approach 1) reduces the adverse effect of inaccurate segmentation, 2) helps to clarify the semantics of a particular region, and 3) enables a simple querying interface for region-based image retrieval systems. The application of SIMPLIcity to several databases, including a database of about 200,000 general-purpose images, has demonstrated that our system performs significantly better and faster than existing ones. The system is fairly robust to image alterations.

AbstractÐIn this paper, we present a clustering and indexing paradigm (called Clindex) for high-dimensional search spaces. The scheme is designed for approximate similarity searches, where one would like to find many of the data points near a target point, but where one can tolerate missing a few near points. For such searches, our scheme can find near points with high recall in very few IOs and perform significantly better than other approaches. Our scheme is based on finding clusters and, then, building a simple but efficient index for them. We analyze the trade-offs involved in clustering and building such an index structure, and present extensive experimental results. Index TermsÐApproximate search, clustering, high-dimensional index, similarity search. 1