Learning to Extract Focused Objects From Low DOF Images

This paper proposes an approach to extract focused objects (i.e., attention objects) from low depth-of-field images. To recognize the focused object, we decompose the image into multiple regions, which are described by using three types of visual descriptors. Each descriptor is extracted from a representation of some aspects of local appearance, e.g., a spatially localized texture, color, or geometrical property. Therefore, the focus detection of a region can be achieved by the classification of extracted visual descriptors based on a binary classifier. We employ a boosting algorithm to learn the classifier with a cascade of decision structure. Given a test image, initial segmentation can be achieved using obtained classification results. Finally, we apply a post-processing technique to improve the results by incorporating region grouping and pixel-level segmentation. Experimental evaluation on a number of images demonstrates the performance advantages of the proposed method, when compared with state-of-the-art methods.