Color Image Segmentation Based on Evidence Theory and Two-Dimensional Histogram
Yan Liu, Deqiang Han, Zhe Zhang, Weifeng Liu, Feihu Zhang · 2018
Image segmentation is one of the most important tasks in image processing and recognition. Image segmentation based on two-dimensional histogram considers not only the target pixel information but also its neighborhood information. It segments the image according to the calculated threshold, which is a hard decision method actually. However, there is uncertainty when labeling the pixels around the threshold. In this paper, we propose a new binary segmentation method for color image based on information fusion. We use two thresholds to model the uncertainty and use Cautious OWA with evidential reasoning (COWA-ER) to implement the fusion-based color image segmentation. Experimental results show that the proposed method achieves better performance compared with the traditional two-dimensional histogram method.