Image segmentation based on complexity mining and mean-shift algorithm

Mean shift algorithm is a well established method for image segmentation. It is particularly popular technique due to non-parametric nature which enables efficient segmentation of complex arbitrary shapes. Despite such advantage, high computational complexity still makes it unsuitable for segmentation of high resolution images in time critical applications. This paper introduces a new approach which alleviates performance issues of mean shift using complexity reduction based on information theory. Proposed algorithm starts by calculating information potential field of the image in order to get insight into complexity of the regions. Afterwards, only complex regions are segmented by computationally expensive mean shift algorithm, while segmentation of simpler regions is performed by a cheaper method. Performance of our method is additionally improved with execution of the key code sections on the GPGPU platform. Experimental results have shown that our method produces comparable segmentation quality to regular parallel mean shift, but with significant reduction in overall execution time.