Image segmentation with low computational burden has been highly regarded as important goal for researchers. One of the popular image segmentation methods is normalized cut algorithm. But it is unfavorable for high resolution image segmentation because the amount of segmentation computation is very huge [1]. To solve this problem, we propose a novel approach for high resolution image segmentation based on the Normalized Cuts. The proposed method preprocesses an image by using the normalized cut algorithm to form segmented regions, and then use k-Means clustering on the regions. The experimental results verify that the proposed algorithm behaves an improved performance comparing to the normalized cut algorithm.

Object extraction, which aims to accurately separate a foreground object from its background in still images, plays an important role in many computer vision applications. An interactive object extraction method based on MSRM (maximal similarity based region merging) is presented in this paper. We can manually mark the target and background only one time in any one image of the image sequence to obtain the object extraction result of the image sequence. Compared to currently used method based on graph cut algorithm that manually marks the target and background on all the images one by one to get the object extraction result, our method is more efficient and the result is as precious as with other methods.