کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
532179 | 869918 | 2013 | 15 صفحه PDF | دانلود رایگان |

• An automatic segmentation approach is proposed for segmenting low DOF images.
• It is time-efficient as it relies on texture details rather than color information.
• A reliable block-based ensemble clustering approach at two levels is developed.
• A threshold is optimized in a difference of Gaussian image.
• We achieve an average F-measure of 91.3% to extract the ROI in an image.
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.
Journal: Pattern Recognition - Volume 46, Issue 10, October 2013, Pages 2685–2699