2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2013)
Portland, OR, USA
June 23, 2013 to June 28, 2013
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CVPR.2013.93
An approach to learn a structured low-rank representation for image classification is presented. We use a supervised learning method to construct a discriminative and reconstructive dictionary. By introducing an ideal regularization term, we perform low-rank matrix recovery for contaminated training data from all categories simultaneously without losing structural information. A discriminative low-rank representation for images with respect to the constructed dictionary is obtained. With semantic structure information and strong identification capability, this representation is good for classification tasks even using a simple linear multi-classifier. Experimental results demonstrate the effectiveness of our approach.
image classification, dictionary learning, low-rank representation
Y. Zhang, Z. Jiang and L. S. Davis, "Learning Structured Low-Rank Representations for Image Classification," 2013 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Portland, OR, USA USA, 2013, pp. 676-683.