Abstract
The tremendous growth in digital imagery has introduced the need for accurate image analysis and classification. The applications include content based image retrieval in the World Wide Web and digital libraries (Dong & Yang, 2002; Heidmann, 2005; Smeulders et al., 2000; Veltkamp et al., 2001) scene classification (Huang et al., 2005; Jiebo et al., 2005), face recognition (Jing & Zhang, 2006; Pentland & Choudhury, 2000; Shen & Bai, 2006) and biological and medical image classification (Awate et al., 2006; Boland & Murphy, 2001; Cocosco et al., 2004; Ranzato et al., 2007). Although attracting considerable attention in the past few years, image classification is still considered a challenging problem in machine learning due to the complexity of real-life images. This chapter discusses an approach to computer vision using automated image classification and similarity measurement based on a large set of general image descriptors. Classification results as well as image similarity measurements are presented for several diverse applications.
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