Object verification in two different views using sparse representation
Shih-Chung Hsu, Chung-Lin Huang · 2015
This paper proposes an object verification method in two different views by using sparse representation. The proposed method contains three major modules. First, we train the sparse matrix by using K-Singular Valued Decomposition (K-SVD) and the maximum correlation training sample selection. Second, we project the training samples onto the sparse matrix to obtain the parse vector training set. Third, we combine two training sets of the same/different objects from two different views to generate positive/negative hybrid sparse vector sets for SVM classifier training. Our contributions in this paper are (1) proposing a better dictionary representation learning than original K-SVD learning, and (2) developing an optimal sparse representation for object verification with very good accuracy. In the experiment, we show that our method has the better accuracy than the other methods.