Color image segmentation using a spatial k-means clustering algorithm
Ilea, Dana E. and Whelan, Paul F. (2006) Color image segmentation using a spatial k-means clustering algorithm. In: IMVIP 2006 - 10th International Machine Vision and Image Processing Conference, 30 August - 1 September 2006, Dublin, Ireland.
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This paper details the implementation of a new adaptive technique for color-texture segmentation that is a generalization of the standard K-Means algorithm. The standard K-Means algorithm produces accurate segmentation results only when applied to images defined by homogenous regions with respect to texture and color since no local constraints are applied to impose spatial continuity. In addition, the initialization of the K-Means algorithm is problematic and usually the initial cluster centers are randomly picked. In this paper we detail the implementation of a novel technique to select the dominant colors from the input image using the information from the color histograms. The main contribution of this work is the generalization of the K-Means algorithm that includes the primary features that describe the color smoothness and texture complexity in the process of pixel assignment. The resulting color segmentation scheme has been applied to a large number of natural images and the experimental data indicates the robustness of the new developed segmentation algorithm.
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