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Graph cut segmentation with a global constraint: recovering region distribution via a bound of the Bhattacharyya measure

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Ben Ayed, Ismail, Hua-mei, Chen, Punithakumar, Kumaradevan, Ross, Ian et Li, Shuo. 2010. « Graph cut segmentation with a global constraint: recovering region distribution via a bound of the Bhattacharyya measure ». In 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (San Francisco, CA, USA, June 13-18, 2010) pp. 3288-3295. IEEE.
Compte des citations dans Scopus : 31.

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Abstract

This study investigates an efficient algorithm for image segmentation with a global constraint based on the Bhattacharyya measure. The problem consists of finding a region consistent with an image distribution learned a priori. We derive an original upper bound of the Bhattacharyya measure by introducing an auxiliary labeling. From this upper bound, we reformulate the problem as an optimization of an auxiliary function by graph cuts. Then, we demonstrate that the proposed procedure converges and give a statistical interpretation of the upper bound. The algorithm requires very few iterations to converge, and finds nearly global optima. Quantitative evaluations and comparisons with state-of-the-art methods on the Microsoft GrabCut segmentation database demonstrated that the proposed algorithm brings improvements in regard to segmentation accuracy, computational efficiency, and optimality. We further demonstrate the flexibility of the algorithm in object tracking.

Item Type: Conference proceeding
ISBN: 1063-6919
Professor:
Professor
Ben Ayed, Ismail
Affiliation: Autres
Date Deposited: 11 Sep 2015 15:28
Last Modified: 03 Feb 2016 01:37
URI: https://espace2.etsmtl.ca/id/eprint/10507

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