Region-of-interest extraction in low depth of field images using ensemble clustering and difference of Gaussian approaches
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.
region-of-interest extraction ensemble clustering low depth-of-field difference of gaussian method expectation-maximization algorithm