Automatic region-of-interest extraction in low depth-of-field images

PhD ThesisAutomatic extraction of focused regions from images with low depth-of-field (DOF) is a problem without an efficient solution yet. The capability of extracting focused regions can help to bridge the semantic gap by integrating image regions which are meaningfully relevant and generally do not exhibit uniform visual characteristics. There exist two main difficulties for extracting focused regions from low DOF images using high-frequency based techniques: computational complexity and performance. A novel unsupervised segmentation approach based on ensemble clustering is proposed to extract the focused regions from low DOF images in two stages. 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, i.e., block-based region-of-interest (ROI), closely conforming to image objects are extracted. In stage two, two different approaches have been developed to extract pixel-based ROI. In the first approach, 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 pixel-based ROI from the map. Experimental results demonstrate that the proposed approach achieves an average segmentation performance of 91.3% and is computationally 3 times faster than the best existing approach. In the second approach, a minimal graph cut is constructed by using the max-flow method and also by using object/background seeds provided by the ensemble clustering algorithm. Experimental results demonstrate an average segmentation performance of 91.7% and approximately 50% reduction of the average computational time by the proposed colour based approach compared with existing unsupervised approaches

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

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