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OCRS: an interactive object-based image clustering and retrieval system

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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, an unsupervised segmentation method called WavSeg is used 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 object-based image retrieval. In the learning and retrieval module, the Diverse Density algorithm is adopted to analyze the user’s interest and generate the initial hypothesis which provides a prototype for future 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 the 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.

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Authors and Affiliations

  1. Department of Computer and Information Sciences, University of Alabama at Birmingham, Birmingham, AL, 35294-1170, USA

    Chengcui Zhang & Xin Chen

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  1. Chengcui Zhang
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  2. Xin Chen
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Correspondence to Chengcui Zhang.

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Zhang, C., Chen, X. OCRS: an interactive object-based image clustering and retrieval system. Multimed Tools Appl 35, 71–89 (2007). https://doi.org/10.1007/s11042-007-0116-9

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