Please use this identifier to cite or link to this item: https://doi.org/10.1109/CVPR.2010.5539967
Title: Visual classification with multi-task joint sparse representation
Authors: Yuan, X.-T. 
Yan, S. 
Issue Date: 2010
Source: Yuan, X.-T., Yan, S. (2010). Visual classification with multi-task joint sparse representation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition : 3493-3500. ScholarBank@NUS Repository. https://doi.org/10.1109/CVPR.2010.5539967
Abstract: We address the problem of computing joint sparse representation of visual signal across multiple kernel-based representations. Such a problem arises naturally in supervised visual recognition applications where one aims to reconstruct a test sample with multiple features from as few training subjects as possible. We cast the linear version of this problem into a multi-task joint covariate selection model [15], which can be very efficiently optimized via kernelizable accelerated proximal gradient method. Furthermore, two kernel-view extensions of this method are provided to handle the situations where descriptors and similarity functions are in the form of kernel matrices. We then investigate into two applications of our algorithm to feature combination: 1) fusing gray-level and LBP features for face recognition, and 2) combining multiple kernels for object categorization. Experimental results on challenging real-world datasets show that the feature combination capability of our proposed algorithm is competitive to the state-of-theart multiple kernel learning methods. ©2010 IEEE.
Source Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
URI: http://scholarbank.nus.edu.sg/handle/10635/72171
ISBN: 9781424469840
ISSN: 10636919
DOI: 10.1109/CVPR.2010.5539967
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