Hill-manipulation: An effective algorithm for color image segmentation

In cluster-based image segmentation techniques, clusters may be viewed as hills in a histogram. These techniques may suffer from large hills that dominate the smaller hills and thus they result in a loss of image details in the segmentation process. In this paper, we propose a novel approach, called Hill-Manipulation algorithm, to solve the problems of the traditional cluster-based image segmentation methods. It starts by segmenting the 3D color histogram into hills according to the number of local maxima found, and then each hill is checked against defined criteria for possible splitting into more homogenous smaller hills. As a result, details of an image are distinguished and the details are captured in the segmentation. Finally, the resulting hills undergo a post-processing task that filters out the small non-significant regions.

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