Video object segmentation based on mixtures of probabilistic principal component analysis

A novel video object segmentation algorithm is proposed based on mixtures of probabilistic principal component analysis (MPPCA) in this paper. The number of mixture components of MPPCA is estimated and the expectation maximization (EM) algorithm is initialized through segmentation projection after extracting feature. Then the EM algorithm is applied to estimate the distribution of feature vectors. Finally the segmentation is carried out by clustering each pixel into appropriate component according to maximum likelihood criterion. The proposed algorithm can greatly accelerate the convergence of the EM algorithm since the initial value approximates its real value. As a result, the speed of the video object segmentation is improved. Experimental results have demonstrated that the proposed method can extract moving objects from video sequences successfully. At the same time, the algorithm proposed is more stable.