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SIMPLIcity: Semantics-sensitive Integrated Matching
for Picture LIbraries

James Z. Wang
The Pennsylvania State University (*)

Jia Li
The Pennsylvania State University (*)

Gio Wiederhold
Stanford University
Abstract:

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 (Semantics-sensitive 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 region-based 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, graph-photograph. 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 {\it 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.

(*) The research work was done when Dr. James Z. Wang and Dr. Jia Li were with Stanford University.


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Citation: James Z. Wang, Jia Li and Gio Wiederhold, ``SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Libraries,'' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 9, pp. 947-963, 2001.

2001 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Last Modified: January 10 2001
@copy; 2001, James Z. Wang and Jia Li