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Top1. Introduction
With the rapid growth of digital and information technologies, more and more multimedia data are generated and made available in digital form. Searching and retrieving relevant images in this huge volume of data is a difficult task and has created an urgent need to develop new tools and techniques. One such solution is the Content Based Image Retrieval (CBIR). As the image databases grow larger, the traditional keyword-based approach for retrieving a particular image becomes inefficient and suffers from the following limitations (i) Vast amount of labor is required for manual image annotation and (ii) Limited capacity for retrieving the visual content of the image and subjectivity of human perception. Hence, to overcome these difficulties of manual annotation approach, content based image retrieval has emerged. CBIR is a collection of techniques and algorithms which enable querying the image databases with low level image content such as color, texture, objects and their geometries rather than textual attributes such as image name or other keywords (Rangachar Kasturi & Jain, 2002). Many image retrieval systems have been developed using all or some of these features. It includes Chabot (Ogle & Stonebraker, 1995), Photobook (Pentland, Picard, & Sclaroff, 1996), QBIC (Flickner et al., 1995), Virage (Batch et al., 1996), VisualSeek (Smith & Chang, 1997), MARS (Huang, Mehrotra, & Ramachandran, 1996), Netra (Ma & Manjunath, 1995), and Excalibur (Feder, 1997). The extensive literature and the state of art methods about content based image retrieval can be found in Datta, Li, and Wang (2005), Rui, Huang, and Chang (1999), Smeulders, Worring, Santini, Gupta, and Jain (2000), Kherfi, Ziou, and Bernardi (2004), Lew, Sebe, Djeraba, and Jain (2006), and Kokare, Chatterji, and Biswas (2002).