Please use this identifier to cite or link to this item:
|Title:||Visual classification with multitask joint sparse representation|
|Citation:||Yuan, X.-T., Liu, X., Yan, S. (2012). Visual classification with multitask joint sparse representation. IEEE Transactions on Image Processing 21 (10) : 4349-4360. ScholarBank@NUS Repository. https://doi.org/10.1109/TIP.2012.2205006|
|Abstract:||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. © 1992-2012 IEEE.|
|Source Title:||IEEE Transactions on Image Processing|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Mar 19, 2019
WEB OF SCIENCETM
checked on Mar 4, 2019
checked on Mar 16, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.