Automatic image annotation via local sparse coding

Sparse coding is an active research topic in machine learning and signal processing community. In this paper, we propose a novel local sparse model for multi-label image annotation. Existing feature descriptors and extraction algorithms pay less attention to semantic information and extracted feature dimension usually is high, which leads to heavy computation. Noise and redundant information often reduce the performance of sparse model. To address these issues, we combine label and visual information for feature selection while most previous work only utilizes labels and ignores visual information itself. First of all, we make use of label sets to seek images neighbor relations and generate the Gaussian kernel matrix over these neighbor images, then use LLP(Local Learning Projection) algorithm to get minimal local estimation error. After that, for each query image, we find its K nearest neighbors in the transformed space and use these neighbors to reconstruct it via sparse coding. Moreover, during coding, we penalize the corresponding reconstruction coefficients to implicitly reflect the neighbor relations. Finally, propagating tags from training data to test data. Image annotation experiments on the Corel5k dataset show the performance of our approach is comparable to several state-of-the-art algorithms.