Video object segmentation based on multi-feature clustering

As a requisite of the emerging content-based multimedia technologies, video object segmentation is of great importance. This paper proposed a method of video object segmentation based on multi-feature clustering. At first, gain the twice-difference image from the three successive video frames. Then, eliminate the noise of background with the estimation of the feature parameter and extract the video object motion area. Afterward, employ the improved FCM clustering method to segment the motion area and get the video object mask by processing the previous result with morphological method. Finally, acquire the ideal video object. Experimental results show that the proposed method performs excellently for video object segmentation and outperforms the method of literature in spatial accuracy.