Image segmentation based on hierarchical mapping

In this paper, an efficient image segmentation technique is presented, which combines an image segmentation algorithm with pyramidal approach to form a scale space representation. First, the coarsest level of pyramidal image is quantized to coarse color space. Then, the segmentation is achieved using, JSEG algorithm followed by region merging, for further refinement. Finally, hierarchical mapping is performed to determine region boundaries in a coarse-to-fine manner using combined global and local features until the final segmentation is accomplished. The multi-resolution approach of the proposed algorithm not only offers a significant reduction in computational cost, but also helps reducing the over-segmentation problem of region growing and watershed techniques. The experimental results show good segmentation performance over a variety of images, also great reduction in the amount of processing time.