Unsupervised Video Object Segmentation Based on Mixture Models and Saliency Detection

In this paper, we propose an unsupervised video object segmentation approach which is mainly based on a saliency detection method and the Gaussian mixture model with Markov random field. In our approach, the saliency detection method is developed as a preprocessing technique to calculate the probability of each pixel as the target object. In contrast to traditional saliency detection methods which are normally difficult to obtain the object's precise boundary and are therefore hard to segment consistent objects, the developed saliency detection method can calculate the saliency of each frame in the video sequence and extract the position and region of the target object with more accurate object boundary. The refined extracted object region is then taken as the prior information and incorporated into the Gaussian mixture model with Markov random field to obtain the precise pixel-wise segmentation result of each frame. The effectiveness of the proposed unsupervised video object segmentation approach is validated through experimental results using both the SegTrack and the SegTrack v2 data sets.