关键词: 超像素分割, K-D树, 混合高斯模型, 图割算法, 光流法

Abstract: Due to random movement of the segmented target, rapid change of background, arbitrary variation and shape deformation of object appearance, in this paper, a new unsupervised video segmentation algorithm based on multiple spatial-temporal feature representations was presented. By combination of salient features and other features obtained from pixels and superpixels, a coarse-to-fine-grained robust feature representation was designed to represent each frame in a video sequence. Firstly, a set of superpixels was generated to represent foreground and background in order to improve computational efficiency and get segmentation results by graph-cut algorithm. Then, the optical flow method was used to propagate information between adjacent frames, and the appearance of each superpixel was updated by its non-local sptatial-temporal features generated by nearest neighbor searching method with efficient K-Dimensional tree (K-D tree) algorithm, so as to improve robustness of segmentation. After that, for segmentation results generated in superpixel-level, a new Gaussian mixture model based on pixels was constructed to achieve pixel-level refinement. Finally, the significant feature of image was introduced, as well as segmentation results generated by graph-cut and Gaussian mixture model, to obtain more accurate segmentation results by voting scheme. The experimental results show that the proposed algorithm is a robust and effective segmentation algorithm, which is superior to most unsupervised video segmentation algorithms and some semi-supervised video segmentation algorithms.

Key words: superpixel segmentation, K-Dimensional tree (K-D tree), Gaussian Mixture Model (GMM), graph-cut algorithm, optical flow method