The paper presents a novel model named Category Topic Simplex-Latent Dirichlet Allocation(CTS-LDA) based on extending LDA(Latent Dirichlet Allocation)

which is used to learn and recognize natural scene category. Unlike previous work

our model can absorb category information in the model inference under learning process to produce its semantic simplex for each category scene. As a result

each category has its own semantic topic simplex

and each image can choose its simplex to denote

which is consistent with people’s cognitive pattern. In previous work

recognizing scene category task need to resort to additional classifier after getting images topic representation. Our method can use ML method to recognize image category during the same time of getting topic representation. Furthermore

we also analyze the influence of the topic size in our model

and infer the fittest result to produce the best performance. We investigate the classification performance under changed scene category tasks. The experiments have demonstrated that our model can perform better with less training data than other methods.