Stopping Region-Based Image Segmentation at Meaningful Partitions

This paper proposes a new stopping criterion for automatic image segmentation based on region merging. The criterion is dependent on image content itself and when combined with the recently proposed approaches to syntactic segmentation can produce results aligned with the most salient semantic regions/objects present in the scene across heterogeneous image collections. The method identifies a single iteration from the merging process as the stopping point, based on the evolution of an accumulated merging cost during the complete merging process. The approach is compared to three commonly used stopping criteria: (i) required number of regions, (ii) value of the least link cost, and (iii) Peak Signal to Noise Ratio (PSNR). For comparison, the stopping criterion is also evaluated for a segmentation approach that does not use syntactic extensions. All experiments use a manually generated segmentation ground truth and spatial accuracy measures. Results show that the proposed stopping criterion improves segmentation performance towards reflecting real-world scene content when integrated into a syntactic segmentation framework.