A novel image classifier based on Gaussian mixture language model

In this paper, we propose a novel Gaussian Mixture Language Model to address the issues of the traditional bag of visual words (BoVW) based model. We firstly take full advantage of image semantic information to learn a new distance metric which can achieve the minimal loss of image information, and then we train Gaussian Mixture Models (GMM) using this distance metric. Given a test image, a visual document is firstly constructed using this codebook, and then its category is determined by estimating the maximum probability using the language model under a specific category. Experiments show that the codebook generated by our method can effectively reflect the image semantic information and highly suitable the language model, and confirm that the proposed method is satisfactory and competitive in comparison with the traditional BoVW based method as well as other state of the art methods.