Abstract

An approach to learn a structured low-rank representation for image classifification 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 identifification capability, this representation is good for classifification tasks even using a simple linear multi-classififier. Experimental results demonstrate the effectiveness of our approach.