Region-of-interest extraction in low depth of field images using ensemble clustering and difference of Gaussian approaches

Rafiee, Gholamreza, Dlay, Satnam and Woo, Wai Lok (2013) Region-of-interest extraction in low depth of field images using ensemble clustering and difference of Gaussian approaches. Pattern Recognition, 46 (10). pp. 2685-2699. ISSN 0031-3203

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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.

Item Type:	Article
Uncontrolled Keywords:	Low depth-of-field, Difference of Gaussian method, Ensemble clustering, Expectation-maximization algorithm, Region-of-interest extraction
Subjects:	G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Paul Burns
Date Deposited:	07 May 2019 09:08
Last Modified:	10 Oct 2019 19:16
URI:	http://nrl.northumbria.ac.uk/id/eprint/39196

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