Implementation of Non Shannon Entropy Measures for Color Image Segmentation And Comparison With Shannon Entropy Measures

Segmentation of color images is important for various image analysis problems and is somewhat more involved than the segmentation of gray scale images. Preserving the original colors in different segments of the original image is one of the main problems in color image segmentation. Here in this paper, presents an approach for color image segmentation, which almost preserves the colors in different segments of input color image. In this projected approach, threshold selection in each of the three component (RGB) images is done on the basis of different entropy measures such as the Shannon, Renyi, Havrda-Charvat, Kapur and Vajda entropy measures. Simulation results for all above mentioned entropy measures are also presented and it is observed that Havrda Charvat and Shannon entropy measures are better than other measures for color image segmentation problems