Accuracy enhancement of image segmentation using adaptive anisotropic diffusion
Jae Sung Lim, Sung In Cho, Young Hwan Kim · 2013
This paper proposes a new pre-processing method to enhance accuracy of image segmentation. The proposed method produces a de-textured image which gives appropriate help to improve the segmentation quality when the existing segmentation method, histogram-based clustering, is applied on the simplified image. For obtaining this simplified image, we perform the de-texturing using an adaptive anisotropic diffusion model. Then, the histogram-based clustering is performed on the de-textured image to obtain segmentation results. In the experiments the Berkeley Segmentation Dataset, probabilistic rand index (PRI) and segmentation covering (SC) values are used for evaluating the segmentation quality. Experimental results showed that the segmentation accuracy of the histogram-based clustering was improved by using pre-processing in terms of average PRI and SC values by up to 0.86%, 14%, respectively.