Color Image Segmentation Combining Rough Depth Information

Wen Su, Jing Qian, Zhiming Pi, Zengfu Wang

Abstract: A novel color image segmentation method is presented in this paper. Firstly a Luv color histogram based method is used to estimate the color bandwidth, then a mean shift algorithm with adaptive color bandwidth is employed to pre-segment the input image. Next, a boundary detection algorithm based machine learning is used to calculate the probability boundary of objects from both depth and color information. Then, a correction procedure is performed by mapping the depth boundary onto the color image. Finally, Graph cut is used to segment color image based on Gaussian Mixture Model which is built with the above pre-segmentation and correction results. The experimental results show that the segmentation algorithm is an effective one. It can effectively segment an image into some semantic objects.