A new multi-criteria fusion model for color textured image segmentation

Fusion of image segmentations using consensus clustering and based on the optimization of a single criterion (commonly called the median partition based approach) may bias and limit the performance of an image segmentation model. To address this issue, we propose, in this paper, a new fusion model of image segmentation based on multi-objective optimization which aims to avoid the bias caused by a single criterion and to achieve a final improved segmentation. The proposed fusion model combines two conflicting and complementary segmentation criteria, namely; the region-based variation of information (VoI) criterion and the contour-based F-Measure (precision-recall) criterion with an entropy-based confidence weighting factor. To optimize our energy-based model we use an optimization procedure derived from the iterative conditional modes (ICM) algorithm. The experimental results on the Berkeley database with manual ground truth segmentations clearly show the effectiveness and the robustness of our multi-objective median partition based approach.