This paper focuses on the problem of automatic image classification （AIC） by proposing a framework based on latent semantic analysis （LSA） and image region pairs. The novel framework employs relative spatial arran...This paper focuses on the problem of automatic image classification （AIC） by proposing a framework based on latent semantic analysis （LSA） and image region pairs. The novel framework employs relative spatial arrangements for region pairs as the primary feature to capture semantics. The significance of this paper is twofold. Firstly, to the best our knowledge, this is the first study of the influence of region pairs as well as their relative spatial information in latent semantic analysis as applied to automatic image classification. Secondly, our proposed method for using the relative spatial information of region pairs show great promise in improving image semantic classi- fication compared with the classical latent semantic analysis method and 2D string representation algorithm.展开更多
文摘This paper focuses on the problem of automatic image classification （AIC） by proposing a framework based on latent semantic analysis （LSA） and image region pairs. The novel framework employs relative spatial arrangements for region pairs as the primary feature to capture semantics. The significance of this paper is twofold. Firstly, to the best our knowledge, this is the first study of the influence of region pairs as well as their relative spatial information in latent semantic analysis as applied to automatic image classification. Secondly, our proposed method for using the relative spatial information of region pairs show great promise in improving image semantic classi- fication compared with the classical latent semantic analysis method and 2D string representation algorithm.
