Mean Shift Segmentation - Evaluation of Optimization Techniques

The mean shift algorithm is a powerful clustering technique, which is based on an iterative scheme to detect modes in a probability density function. It has been utilized for image segmentation by seeking the modes in a feature space composed of spatial and color information. Although the modes of the feature space can be efficiently calculated in that scheme, different optimization techniques have been investigated to further improve the calculation speed. Besides those techniques that improve the efficiency using specialized data structures, there are other ones, which take advantage of some heuristics, and therefore affect the accuracy of the algorithm output. In this paper we discuss and evaluate different optimization strategies for mean shift based image segmentation. These optimization techniques are quantitatively evaluated based on different real world images. We compare segmentation results of heuristic-based, performance-optimized implementations with the segmentation result of the original mean shift algorithm as a gold standard. Towards this end, we utilize different partition distance measures, by identifying corresponding regions and analyzing the thus revealed differences.