This paper presented a comparative study of human skin color detection HSV and YCbCr color space. Skin color detection is the process of separation between skin and non-skin pixels. It is difficult to develop uniform method for the segmentation or detection of human skin detection because the color tone of human skin is drastically varied for people from one region to another. Literature survey shows that there is a variety of color space is applied for the skin color detection. RGB color space is not preferred for color based detection and color analysis because of mixing of color (chrominance) and intensity (luminance) information and its non uniform characteristics. Luminance and Hue based approaches discriminate color and intensity information even under uneven illumination conditions. Experimental result shows the efficiency of YCbCr color space for the segmentation and detection of skin color in color images.

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Segmentation is the process of partitioning an image into number of meaningful images as segments or clusters. The segmentation is initial but important process which is used to locate boundaries and objects in images. This paper is concerned with segmentation of color satellite images using neural network based kohonen's self-organizing maps. This unsupervised competitive network is used to visualize and interpret large data sets. In this paper, test images are segmented in RGB and HSV color space using self-organizing map and the segmentation results are compared using error image, peak signal to noise ratio, and execution time. The efficiency of proposed method is tested with Landsat and Terra (MODIS sensor) satellite images.

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