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Visual Classification With Multitask Joint Sparse Representation

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3 Author(s)
Xiao-Tong Yuan ; Department of Statistics, Rutgers University, Newark, NJ, USA ; Xiaobai Liu ; Shuicheng Yan

We address the problem of visual classification with multiple features and/or multiple instances. Motivated by the recent success of multitask joint covariate selection, we formulate this problem as a multitask joint sparse representation model to combine the strength of multiple features and/or instances for recognition. A joint sparsity-inducing norm is utilized to enforce class-level joint sparsity patterns among the multiple representation vectors. The proposed model can be efficiently optimized by a proximal gradient method. Furthermore, we extend our method to the setup where features are described in kernel matrices. We then investigate into two applications of our method to visual classification: 1) fusing multiple kernel features for object categorization and 2) robust face recognition in video with an ensemble of query images. Extensive experiments on challenging real-world data sets demonstrate that the proposed method is competitive to the state-of-the-art methods in respective applications.

Published in:

IEEE Transactions on Image Processing  (Volume:21 ,  Issue: 10 )