Locality-Constrained Low-Rank Coding for Image Classification

Low-rank coding (LRC), originated from matrix decomposition, is recently introduced into image classification. Following the standard bag-of-words (BOW) pipeline, when coding the data matrix in the sense of low-rankness incorporates contextual information into the traditional BOW model, this can capture the dependency relationship among neighbor patches. It differs from the traditional sparse coding paradigms which encode patches independently. Current LRC-based methods use l(1) norm to increase the discrimination and sparseness of the learned codes. However, such methods fail to consider the local manifold structure between data space and dictionary space. To solve this problem, we propose a locality-constrained low-rank coding (LCLR) algorithm for image representations. By using the geometric structure information as a regularization term, we can obtain more discriminative representations. In addition, we present a fast and stable online algorithm to solve the optimization problem. In the experiments, we evaluate LCLR with four benchmarks, including one face recognition dataset (extended Yale B), one handwritten digit recognition dataset (USPS), and two image datasets (Scene13 for scene recognition and Caltech101 for object recognition). Experimental results show that our approach outperforms many state-of-the-art algorithms even with a linear classifier.