OCRS: an Interactive Object-based Image Clustering and Retrieval System

Authors:
Chengcui Zhang;Xin Chen
Affiliations:
University of Alabama at Birmingham, Birmingham, AL;University of Alabama at Birmingham, Birmingham, AL
Venue:
MDM '05 Proceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data
Year:
2005

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Abstract

In this paper, we propose an Interactive Object-based Image Clustering and Retrieval System (OCRS). The system incorporates two major modules: Preprocessing and Object-based Image Retrieval. In preprocessing, we use WavSeg to segment images into meaningful semantic regions (image objects). This is an area where a huge number of image regions are involved. Therefore, we propose a Genetic Algorithm based algorithm to cluster these images objects and thus reduce the search space for image retrieval. In learning and retrieval module, Diverse Density is adopted to analyze user's interest and generate the initial hypothesis which provides a prototype for later learning and retrieval. Relevance Feedback technique is incorporated to provide progressive guidance to the learning process. In interacting with user, we propose to use One-Class Support Vector Machine (SVM) to learn user's interest and refine the returned result. Performance is evaluated on a large image database and the effectiveness of our retrieval algorithm is demonstrated through comparative studies.