Learning spatio-temporal dependency of local patches for complex motion segmentation

Segmenting complex motion, such as articulated motion and deformable objects, can be difficult if the prior knowledge of the motion pattern is not available. We present a novel method for motion segmentation by learning the motion priors from exemplar motions to guide the segmentation. Instead of modeling the motion field explicitly, we decompose each video frame into a number of local patches and learn the spatio-temporal contextual relations among them, e.g., if their motion relationships are consistent with that from the training data. Based on a novel motion feature to measure the relative motion of two patches, the SVM classifier learns their pairwise relationship. We convert the motion segmentation problem to a binary labeling problem, and propose an iterative solution to group the local patches whose motions are consistent. Compared with other approaches, such as the graph cut and normalized cut methods, this new method is computationally more efficient and is able to better handle the inaccurate inference of pairwise relationships. Results on both synthesized and real videos show that our method can learn to segment different types of complex motion patterns.

Research highlights
► We learn motion-specific priors from exemplars to regularize motion segmentation.
► Different motion priors lead to different segmentation results.
► A new feature is designed to describe the motion relation between two image patches.
► Our method outperforms graph cuts and normalized cuts.
► This method can deal with textured/complex motion patterns and is robust to noise.