Harmony Potentials - Fusing Global and Local Scale for Semantic Image Segmentation

The Hierarchical Conditional Random Field (HCRF) model have been successfully applied to a number of image labeling problems, including image segmentation. However, existing HCRF models of image segmentation do not allow multiple classes to be assigned to a single region, which limits their ability to incorporate contextual information across multiple scales. At higher scales in the image, this representation yields an oversimplified model since multiple classes can be reasonably expected to appear within large regions. This simplified model particularly limits the impact of information at higher scales. Since class-label information at these scales is usually more reliable than at lower, noisier scales, neglecting this information is undesirable. To address these issues, we propose a new consistency potential for image labeling problems, which we call the harmony potential. It can encode any possible combination of labels, penalizing only unlikely combinations of classes. We also pr...

• Type: Journal
• Year: 2012
• Where: IJCV
• Authors: Xavier Boix, Josep M. Gonfaus, Joost van de Weijer, Andrew D. Bagdanov, Joan Serrat Gual, Jordi Gonzàlez
• Keywords: Computer Networks, Ed Model, IJCV 2012, Image Segmentation, Sampling Strategy