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 [1]) for improving the correspondence of segmentation results produced by the well-known Recursive Shortest Spanning Tree (RSST) algorithm [2] 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 [3] 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.

• Type: Conference
• Year: 2007
• Where: ICIP
• Authors: Tomasz Adamek, Noel E. O'Connor
• Keywords: ICIP 2007, Image Processing, Semantic Object, Shortest Spanning Tree, Syntactic Visual Features