Improved mean shift segmentation approach for natural images

This paper proposes an improved natural image segmentation approach that is more effective, more controllable and more stable under various backgrounds than the traditional mean shift segmentation. The proposed approach employs following four new aspects: the changeable color bandwidth, the direct density searching, the global optimization for mode merging, and the elimination of texture patches. In bandwidth selection, the optimal color bandwidth under Plug-in rule used by the traditional approach is not suitable for actual vision tasks, and a changeable color bandwidth makes it easy to control the segmentation result. The performance of the direct density searching is better than that of mean shift under the same spatial bandwidth. A global optimization criterion for mode merging stabilizes the segmentation result of different images. The elimination of texture patches mostly removes the small patches resulting from texture. In addition, after mode detection, an image is partitioned into some local patches, each of which corresponds to a local mode. These patches are got with color information, and they can be taken as the initial segmentation for further processing that is based on a global optimization criterion constructed by texture features.