Hybrid Sparse Subspace Clustering for Visual Tracking

Lin Ma, Zhihua Liu

Abstract: In many conditions, the object samples are distributed in a number of different subspaces. By segmenting the sub-spaces with spectral clustering based subspace clustering, more accurate sample distribution is obtained. The LSR (Least Squares Regression) sparse subspace clustering method which fulfills the EBD (Enhance Block Diagonal) criterion and has closed-form solution, is an important spectral clustering based sparse subspace clustering method. However, LSR uses no discriminative information which is important to discriminate positive samples from the negative samples. Thus, we propose a new hybrid sparse subspace clustering method which makes the clustering discriminative by involving the discriminative information provided by graph embedding into LSR. The sub subspaces obtained based on the new subspace clustering method can both retain the object distribution information and also make the object samples less confused with surrounding environment. Experimental results on a set of challenging videos in visual tracking demonstrate the effectiveness of our method in discriminating the object from the background.