A Robust Color Object Analysis Approach to Efficient Image Retrieval
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A Robust Color Object Analysis Approach to Efficient Image Retrieval Ruofei Zhang Department of Computer Science, State University of New York, Binghamton, NY 13902, USA Email: [email protected]
Zhongfei (Mark) Zhang Department of Computer Science, State University of New York, Binghamton, NY 13902, USA Email: [email protected] Received 20 December 2002; Revised 1 December 2003 We describe a novel indexing and retrieval methodology integrating color, texture, and shape information for content-based image retrieval in image databases. This methodology, we call CLEAR, applies unsupervised image segmentation to partition an image into a set of objects. Fuzzy color histogram, fuzzy texture, and fuzzy shape properties of each object are then calculated to be its signature. The fuzzification procedures effectively resolve the recognition uncertainty stemming from color quantization and human perception of colors. At the same time, the fuzzy scheme incorporates segmentation-related uncertainties into the retrieval algorithm. An adaptive and effective measure for the overall similarity between images is developed by integrating properties of all the objects in every image. In an effort to further improve the retrieval efficiency, a secondary clustering technique is developed and employed, which significantly saves query processing time without compromising retrieval precision. A prototypical system of CLEAR, we developed, demonstrated the promising retrieval performance and robustness in color variations and segmentationrelated uncertainties for a test database containing 10 000 general-purpose color images, as compared with its peer systems in the literature. Keywords and phrases: content-based image retrieval, fuzzy logic, region-based features, object analysis, clustering, efficiency.
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INTRODUCTION
The dramatic improvements in hardware technology have made it possible in the last few years to process, store, and retrieve huge amount of data in image databases. Initial attempts to manage pictorial documents relied on textual description provided by a human operator. This timeconsuming approach rarely captures the richness of visual content of the images. For this reason researchers have focused on the automatic extraction of the visual content of images to enable indexing and retrieval, in other word, content-based image retrieval (CBIR). CBIR is aimed at efficient retrieval of relevant images from large image databases based on automatically derived features. These features are typically extracted from shape, texture, and/or color properties of query image and images in the database. The relevancies between a query image and images in the database are ranked according to a similarity measure computed from the features. In this paper we describe an efficient clustering-based fuzzy feature representation approach—clustering-based ef-
ficient automatic region analysis technique, as we conveniently named CLEAR, to address general purposed CBIR. We integrate semantic-intensive clustering-based segmentation with fuzzy
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