The effectiveness of content-based image retrieval can be enhanced using heterogeneous features embedded in the images. However, since the features in texture, color, and shape are generated using different computation methods and thus may require different similarity measurements, the integration of the retrievals on heterogeneous features is a nontrivial task. We present a semantics-based clustering and indexing approach, termed SemQuery, 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 subclusters that are represented by the heterogeneous features that the images contain. An image is included in a semantic cluster if it falls within the scope of all the heterogeneous clusters of the semantic cluster. We also design a neural network model to merge the results of basic queries on individual features. A query processing strategy is then presented to support visual queries on heterogeneous features. An experimental analysis is conducted and presented to demonstrate the effectiveness and efficiency of the proposed approach.

The effectiveness of the content-based image retrieval can be enhanced using the heterogeneous features embedded in the images. However, since the features in texture, color, and shape are generated using different computation methods and thus may require different similarity measurements, the integration of the...

With the advances in multimedia databases and the popularization of the Internet, it is now possible to access large image and video repositories distributed throughout the world. One of the challenging problems in such access is how the information in the respective databases can be summarized to enable an intelligent selection of relevant database sites based on visual queries. This paper presents an approach to solve this problem based on image content-based indexing of a metadatabase at a query distribution server. The metadatabase records a summary of the visual content of the images in each database through image templates and statistical features characterizing the similarity distributions of the images. The selection of the databases is done by searching the metadatabase using a ranking algorithm that uses the query's similarity to a template and the features of the databases associated with the template. Two selection approaches, termed mean-based and histogram-based approaches, are presented. The database selection mechanisms have been implemented in a metaserver, and extensive experiments have been performed to demonstrate the effectiveness of the database selection approaches.

made available at other locations for purposes ranting from diagnostics, distance learning, to electronic c;rnI merce.Accessing these repositories in a distributed setting over either a proprietary or a public network poses a number of challenges.With the advances in multimedia databases and the popularization of the Internet, it is now possible to access large image and video repositories distributed throughout the world.One of the challenging problems in such an access is how the information in the respective databases can be summarized to enable an intelligent selection of relevant database sites based on visual queries.This paper presents an approach to this problem that is based on image content-based indexing of a metadatabase at a query distribution server.The metadatabase records a summary of the visual content of the images per database through image templates and statistical features characterizing the similarity value distributions of images.The selection of databases is done by searching the metadatabase using a histogram-based ranking algorithm that uses query similarity to a template and the features of databases associated with the template.The database selection mechanism has been implemented as a metaserver and extensive experiments have been done to demonstrate the effectiveness of the database ranking algorithm.