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Region-of-Interest Extraction in Low Depth of Field Images Using Ensemble Clustering and Difference of Gaussian Approaches

Lookup NU author(s): Dr Reza Rafiee, Professor Satnam Dlay, Dr Wai Lok Woo

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Abstract

In this paper, a two-stage unsupervised segmentation approach based on ensemble clustering is proposed to extract the focused regions from low depth-of-field (DOF) images. The first stage is to cluster image blocks in a joint contrast-energy feature space into three constituent groups. To achieve this, we make use of a normal mixture-based model along with standard expectation-maximization (EM) algorithm at two consecutive levels of block size. To avoid the common problem of local optima experienced in many models, an ensemble EM clustering algorithm is proposed. As a result, relevant blocks closely conforming to image objects are extracted. In stage two, a binary saliency map is constructed from the relevant blocks at the pixel level, which is based on difference of Gaussian (DOG) and binarization methods. Then, a set of morphological operations is employed to create the region-of-interest (ROI) from the map. Experimental results demonstrate that the proposed approach achieves an F-measure of 91.3% and is computationally 3 times faster than the existing state-of-the-art approach.


Publication metadata

Author(s): Rafiee G, Dlay SS, Woo WL

Publication type: Article

Publication status: Published

Journal: Pattern Recognition

Year: 2013

Volume: 46

Issue: 10

Pages: 2685-2699

Print publication date: 03/04/2013

ISSN (print): 0031-3203

ISSN (electronic): 1873-5142

Publisher: Elsevier

URL: http://dx.doi.org/10.1016/j.patcog.2013.03.006

DOI: 10.1016/j.patcog.2013.03.006


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