Hill-climbing Algorithm for Efficient Color-based Image Segmentation

T. Ohashi, Z. Aghbari, and A. Makinouchi (Japan)


Color-based image segmentation, Hill-Climbing Algorithm.


In this paper, we present a novel image segmentation method that produces a set of visually coherent regions. The method is based on a hill-climbing approach and achieves the segmentation by performing two main tasks. First, the hill-climbing algorithm detects local maxima of clusters in the global three-dimensional color histogram of an image. Then, the algorithm associates the pixels of an image with the detected local maxima; as a result, several visually coherent segments are generated. The segmentation algorithm is simple and fast. Moreover, the whole segmentation process is performed without any hand-tuning of parameters. This method does not assume any a priori knowledge on the number of clusters or the content of an image.

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