Unsupervised segmentation of focused regions in images with low depth of field

Unsupervised extraction of focused regions from images with low depth-of-field (DOF) is a problem without an efficient solution yet. In this paper, we propose an efficient unsupervised segmentation solution for this problem. The proposed approach which is based on ensemble clustering and graph-cut modeling aims to extract meaningful focused regions from a given image at two stages. In the first stage, a novel two-level based ensemble clustering technique is developed to classify image blocks into three constituent classes. As a result, object and background blocks are extracted. By considering certain pixels of object and background blocks as seeds, a constraint is provided for the next stage of the approach. In stage two, a minimal graph cuts is constructed by utilizing the max-flow method and using object and background seeds. Experimental results demonstrate that the proposed approach achieves an average F-measure of 91.7% and is computationally up to 2 times faster than existing unsupervised approaches.