Image classification and annotation based on robust regularized coding

Abstract

In recent years, sparse coding has been widely applied to construct high-level image representation in computer vision applications. However, one of major deficiencies of sparse coding is that it fails to capture spatial context in the data. Similar descriptors may be quantized into different visual words during feature quantization process. In this paper, we propose a novel coding scheme called robust regularized coding (RRC), which fully exploits the geometrical information among local descriptors to significantly boost the discriminating capability of the resultant features. More specifically, both locality constraint and smoothness constraint terms with respect to RRC codes are incorporated into the objective function to preserve the local invariance of RRC codes. Besides, to scale up to larger databases, a novel online learning algorithm with no hyperparameter tuning is proposed to incrementally update the codebook. The obtained RRC codes are then employed to represent images for classification and annotation tasks in our experiments. We also propose an effective reconstruction-based image annotation algorithm to propagate the labels of training images to test image by multi-label linear embedding. The experimental results extensively evaluated over several benchmarking datasets demonstrate our approach can achieve significant performance improvements with respect to the state of the arts.