Block-based unsupervised natural image segmentation

We propose a block-based unsupervised image segmentation algorithm in which the basic unit for the region labeling is an image block rather than a pixel. Adopting the image block, we can introduce a new paradigm for the unsupervised image segmentation, namely, an edge-and-region based segmentation. That is, in our block-based segmentation, we exploit both edge blocks in the region boundary and the homogeneous blocks inside the region. The interior texture and monotone blocks are used to identify regions and the edge blocks are used to find an accurate contour. To obtain a pixel level segmentation, we divide the edge and unlabeled blocks into quadrant and relabel those sub-blocks to one of the neighboring homogeneous regions. We repeat this process until we obtain a pixel-based segmentation. Experimental results show that the proposed segmentation yields accurate segmentation for natural images even if they contain some texture regions as well as monotone regions.