Image classification based on non-negative locality-constrained linear coding

The most important issue of image classification algorithm based on feature extraction is how to Efficiently encode features. Locality-constrained linear coding (LLC) has achieved the state-of-the-art performance on several bench-marks, due to its underlying properties of better construction and local smooth sparsity. However, the performance of LLC on image classification is sensitive to the number of neighbors, i.e., the value of k. With the increase of k, the absolute difference of some negative and positive elements may likely become larger and larger. This will make LLC more unstable. In this paper, a new coding scheme called non-negative locality-constrained linear coding (NNLLC) is proposed. It adds an extra non-negative constraint to the objective function of LLC. Generally, this new model can be solved by iterative optimization methods, however, such solutions are quite impractical due to high computational cost. Therefore, two fast approximation algorithms are proposed; more importantly, they and LLC have a similar computational complexity. To compare with LLC, the experiment results on several widely used image datasets demonstrate that NNLLC not only can improve the classification accuracy by nearly 1%~4%, but also is more robust on the selection of k.