Abstract: An approach is proposed to obtain the dictionary capacity of bag of words(BoW) model efficiently, which is combined with The Latent Dirichlet Allocation (LDA) model to analyze the performance of scene category. Based on the feature matrix of scene image data sets constructed by SIFT feature, the affinity propagation method is firstly employed to obtain the clustering numbers, and to take the minimal clustering number as the visual dictionary capacity before generating a visual dictionary. Secondly, the scene training and testing sets are described by these visual words. Finally, the LDA model is employed to classify the testing data set. The experiments show that the proposed approach in this paper maintains higher accuracy of scene classification and can improve efficiency greatly.

Key words: bag of words; visual words; visual dictionary; LDA model