Fundamentals of digital image processing
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Computer graphics: principles and practice (2nd ed.)
Computer graphics: principles and practice (2nd ed.)
Pattern recognition: statistical, structural and neural approaches
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Digital Image Processing
Single color extraction and image query
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Fast signature-based color-spatial image retrieval
ICMCS '97 Proceedings of the 1997 International Conference on Multimedia Computing and Systems
Research of image retrieval algorithms based on color
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
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Color histograms are widely used in most of color content-based image retrieval systems to represent color content. However, the high dimensionality of a color histogram hinders efficient indexing and matching. To reduce histogram dimension with the least loss in color content, color space quantization is indispensable. This paper highlights and emphasizes the importance and the objectives of color space quantization. The color conservation property is examined by investigating and comparing different clustering techniques in perceptually uniform color spaces and for different images. For studying color spaces, perceptually uniform spaces, such as the Mathematical Transformation to Munsell system (MTM) and the C.I.E. L*a*b*, are investigated. For evaluating quantization approaches, the uniform quantization, the hierarchical clustering, and the Color-Naming-System (CNS) supervised clustering are studied. For analyzing color loss, the error bound, the quantized error in color space conversion, and the average quantized error of 400 color images are explored. A color-content-based image retrieval application is shown to demonstrate the differences when applying these clustering techniques. Our simulation results suggest that good quantization techniques lead to more effective retrieval.