Global and Local Features Based Topic Model for Scene Recognition

This paper presents a novel Global and Local Features based Latent Dirichlet Allocation model for scene recognition. The proposed model follows the bag-of-word framework like the Latent Dirichlet Allocation model. The traditional Latent Dirichlet Allocation model for scene recognition only uses the orderless bag of features called global features without considering spatial constraints on these features. Different from this model, our proposed model can combine both global features and local region features for improving the recognition performance. In our method, local region features are gotten by adding a simple spatial constraint on the orderless bag of features. Experiments on three scene datasets demonstrate the effectiveness of our proposed model.