Edge-adaptive image segmentation based on seam processing and K-Means clustering

A new image segmentation method is proposed to combine the edge information with the feature-space method, K-Means clustering. A procedure called seam processing, which is computationally ef cient, is employed to search for horizontal and vertical seams that contain edge information. By transforming the spatial coordinates based on the seam detection results, the edge information can be added to the feature vectors, which are the inputs of K-Means algorithm. The experiments show that the proposed method can achieve edge-adaptive segmentation results, which can not be obtained using traditional methods based on K-Means clustering.

• Type: Journal
• Year: 2010
• Where: ICIP
• Authors: Tse-Wei Chen, Hsiao-Hang Su, Yi-Ling Chen, Shao-Yi Chien
• Keywords: Edge Information, ICIP 2010, Image Processing, Image Segmentation Method, K-means