Please use this identifier to cite or link to this item:
|Title:||Sparse representation using nonnegative curds and whey|
|Citation:||Liu, Y.,Wu, F.,Zhang, Z.,Zhuang, Y.,Yan, S. (2010). Sparse representation using nonnegative curds and whey. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition : 3578-3585. ScholarBank@NUS Repository. https://doi.org/10.1109/CVPR.2010.5539934|
|Abstract:||It has been of great interest to find sparse and/or nonnegative representations in computer vision literature. In this paper we propose a novel method to such a purpose and refer to it as nonnegative curds and whey (NNCW). The NNCW procedure consists of two stages. In the first stage we consider a set of sparse and nonnegative representations of a test image, each of which is a linear combination of the images within a certain class, by solving a set of regression-type nonnegative matrix factorization problems. In the second stage we incorporate these representations into a new sparse and nonnegative representation by using the group nonnegative garrote. This procedure is particularly appropriate for discriminant analysis owing to its supervised and nonnegativity nature in sparsity pursuing. Experiments on several benchmark face databases and Caltech 101 image dataset demonstrate the efficiency and effectiveness of our nonnegative curds and whey method. ©2010 IEEE.|
|Source Title:||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Nov 7, 2018
checked on Oct 27, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.