Location-Aware Image Classification

Currently, the most popular image classification methods are based on global image representations. They face an obvious contradiction between the uncertainty of object position and the global image representation. In this paper, we propose a novel location-aware image classification framework to address this problem. In our framework, an image is classified based on local image representation, and the classifier is learned using an iterative multi-instance learning with a latent SVM, i.e., we infer object location using latent SVM to improve image classification. Our method is very efficient and outperforms the popular spatial pyramid matching (SPM) method and the Region Based Latent SVM (RBLSVM) method [1] on the challenging PASCAL VOC dataset.