Object-Based Regions of Interest for Image Compression

A fully automated architecture for object-based region of interest (ROI) detection is proposed. ROI's are defined as regions containing user defined objects of interest, and an efficient algorithm is developed for the detection of such regions. The algorithm is based on the principle of discriminant saliency, which defines as salient the image regions of strongest response to a set of features that optimally discriminate the object class of interest from all the others. It consists of two stages, saliency detection and saliency validation. The first detects salient points, the second verifies the consistency of their geometric configuration with that of training examples. Both the saliency detector and the configuration model can be learned from cluttered images downloaded from the web. Learning and ROI detection are optimal in the minimum probability of error (MPE) sense, and computationally efficient. This enables interactive user training of ROI-based image coders, with minimal amounts of manual supervision. Experimental results are presented for images of complex scenes, containing both objects and background clutter, and demonstrate good object-based ROI image compression performance.