Power Law Dependencies to Detect Regions of Interest

This paper presents a novel approach to detect regions of interest in digital photographic grayscale images using power laws. The method is intended to find regions of interest in various types of unknown images. Either Zipf law or inverse Zipf law are used to achieve this detection. The detection method consists in dividing the image in several sub-images, computing the frequency of occurence of each different image pattern, representing this distribution by a power law model and classifying the sub-frames according to the power law characteristics. Both power laws models allow region of interest detection, however inverse Zipf law has better performances than Zipf law. The detection results are generally consistent with the human perception of regions of interest.