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Spatial Entropy-based Cost Function for Gray and Color Image Segmentation with Dynamic Optimal Partitioning


MK Quweider


Vol. 12  No. 4  pp. 64-76


In this paper, we present a novel thresholding-based segmentation algorithm that combines entropy, image spatial information, and dynamic programming to non-uniformly quantize an image in a more efficient and effective way for subsequent processing. Combined with information related to the structural content present in the image (activity/busyness of pixels with respect to their immediate neighbors), an entropy-based cost function is derived and used with the one-dimensional histogram probability distribution function of the image. The image quantization/ segmentation algorithm uses dynamic programming based on a recently introduced algorithm for optimal partitioning on an interval, and allow the selection of a broad range gray level to be present in the output image binarization of an image is accomplished by having only two gray levels in the output image. Applications of the algorithm to quantization of gray-level as well as color images in the RGB and HSV color spaces are presented. Image simulations give very good results compared to many existing methods, while maintaining low computational complexity in terms of storage and processing requirements.


Entropy, Spatial information, Dynamic programming, Cost functions, Optimal partitioning on an interval, Image segmentation