Fast two-step histogram-based image segmentation

The authors propose a novel image segmentation technique based on the non-parametric clustering procedure in the discretised colour space. The discrete probability density function is estimated in two steps. Multidimensional colour histogram is created, which is afterwards used to acquire final density estimate using the variable kernel density estimation technique. Segmentation is obtained by mapping revealed range domain clusters to the spatial image domain. The proposed method is highly efficient, running in time linear to the number of the image pixels with low constant factors. The output of the algorithm can be accommodated for a particular application to simplify the integration with other image processing techniques. Quantitative evaluation on a standard test dataset proves that the quality of the segmentations provided by the proposed method is comparable to the quality of the segmentations generated by other widely adopted low-level segmentation techniques, while running times are several times faster.

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