Perceptual grouping of segmented regions in color images

Image segmentation is often the 0rst yet important step of an image understanding system. However, general-purpose image segmentation algorithms that do not rely on speci0c object models still cannot produce perceptually coherent segmentation of regions at a level comparable to humans. Over-segmentation and under-segmentation have plagued the research community in spite of many signi0cant advances in the 0eld. Therefore, grouping of segmented region plays a signi0cant role in bridging image segmentation and high-level image understanding. In this paper, we focused on non-purposive grouping (NPG), which is built on general expectations of a perceptually desirable segmentation as opposed to any object speci0c models, such that the grouping algorithm is applicable to any image understanding application. We propose a probabilistic model for the NPG problem by de0ning the regions as a Markov random 0eld (MRF). A collection of energy functions is used to characterize desired single-region properties and pair-wise region properties. The single-region properties include region area, region convexity, region compactness, and color variances in one region. The pair-wise properties include color mean di;erences between two regions; edge strength along the shared boundary; color variance of the cross-boundary area; and contour continuity between two regions. The grouping process is implemented by a greedy method using a highest con0dence 0rst (HCF) principle. Experiments have been performed on hundreds of color photographic images to show the e;ectiveness of the grouping algorithm using a set of 0xed parameters. ? 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.