Image classification using boosted local features with random orientation and location selection

Abstract

The combination of local features with sparse technique has improved image classification performance dramatically in recent years. Although very effective, this strategy still has two shortcomings. First, local features are often extracted in a pre-defined way (e.g. SIFT with dense sampling) without considering the classification task. Second, the codebook is generated by sparse coding or its variants by minimizing the reconstruction error which has no direct relationships with the classification process. To alleviate the two problems, we propose a novel boosted local features method with random orientation and location selection. We first extract local features with random orientation and location using a weighting strategy. This randomization process makes us to extract more types of information for image representation than pre-defined methods. These extracted local features are then encoded by sparse representation. Instead of generating the codebook in a single process, we construct a series of codebooks and the corresponding encoding parameters of local features using a boosting strategy. The weights of local features are determined by the classification performances of learned classifiers. In this way, we are able to combine the local feature extraction and encoding with classifier training into a unified framework and gradually improve the image classification performance. Experiments on several public image datasets prove the effectiveness and efficiency of the proposed method.