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

Rafiee, Gholamreza, Dlay, Satnam and Woo, Wai Lok (2013) Unsupervised segmentation of focused regions in images with low depth of field. In: ICME 2013 - 2013 IEEE International Conference on Multimedia and Expo, 15th - 19th July 2013, San Jose, CA.

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Abstract

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.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Ensemble clustering, expectation-maximization algorithm, graph-cut optimization, interest regions segmentation, low depth-of-field
Subjects:	G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Paul Burns
Date Deposited:	08 May 2019 15:57
Last Modified:	10 Oct 2019 19:15
URI:	http://nrl.northumbria.ac.uk/id/eprint/39227

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