Using Dempster-Shafer Theory to Fuse Multiple Information Sources in Region-Based Segmentation
This paper presents a new method for segmentation of images into large regions that reflect the real world objects present in a scene. It explores the feasibility of utilizing spatial configuration of regions and their geometric properties (the so-called syntactic visual features by C. Ferran Bennstrom and JR Casas (2004)) for improving the correspondence of segmentation results produced by the well-known recursive shortest spanning tree (RSST) algorithm by O.J. Morris et al. (1986) to semantic objects present in the scene. The main contribution of this paper is a novel framework for integration of evidence from multiple sources with the region merging process based on the Dempster-Shafer (DS) theory by P. Smets (1988) that allows integration of sources providing evidence with different accuracy and reliability. Extensive experiments indicate that the proposed solution limits formation of regions spanning more than one semantic object.