Automatic Segmentation of Interest Regions in Low Depth of Field Images Using Ensemble Clustering and Graph Cut Optimization Approaches

Rafiee, Gholamreza, Dlay, Satnam and Woo, Wai Lok (2013) Automatic Segmentation of Interest Regions in Low Depth of Field Images Using Ensemble Clustering and Graph Cut Optimization Approaches. In: 2012 IEEE International Symposium on Multimedia. IEEE, pp. 161-164. ISBN 978-1-4673-4370-1

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Abstract

Automatic segmentation of images with low depth of field (DOF) plays an important role in content-based multimedia applications. The proposed approach aims to separate the important objects (i.e., interest regions) of a given image from its defocused background in two stages. In stage one, image blocks are classified into object and background blocks using a novel cluster ensemble algorithm. By indicating the certain pixels (seeds) of the object and background blocks, a hard constraint is provided for the next stage of the approach. In stage two, a minimal graph cut is constructed using object and background seeds, which is based on the max-flow method. Experimental results for a wide range of busy-texture (i.e., noisy) and smooth regions demonstrate that the proposed approach provides better segmentation performance at higher speed compared with the state-of-the-art approaches.

Item Type:	Book Section
Uncontrolled Keywords:	cluster ensemble, unsupervised segmentation, graph cut optimization, low depth-of-field image
Subjects:	G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Becky Skoyles
Date Deposited:	10 May 2019 11:38
Last Modified:	10 Oct 2019 19:15
URI:	http://nrl.northumbria.ac.uk/id/eprint/39249

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