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Color is one of the main visual cues and has been frequently used in image processing, analysis and retrieval. The extraction of high-level color descriptors is an increasingly important problem, as these descriptions often provide link to image content. When combined with image segmentation color naming can be used to select objects by color, describe the appearance of the image and even generate semantic annotations. For example, regions labeled as light blue and strong green may represent sky and grass, vivid colors are typically found in man-made objects, and modifiers such as brownish, grayish and dark convey the impression of the atmosphere in the scene. This paper presents a computational model for color categorization, naming and extraction of color composition. In this work we start from the National Bureau of Standards recommendation for color names, and through subjective experiments develop our color vocabulary and syntax. Next, to attach the color name to an arbitrary input color, we design a perceptually based color naming metric. Finally, we extend the method and develop a scheme for extracting the color composition of a complex image. The algorithm follows the relevant neurophysiological findings and studies on human color categorization. In testing the method the known color regions in different color spaces were identified accurately, the color names assigned to randomly selected colors agreed with human judgments, and the color composition extracted from natural images was consistent with human observations.