The codebook is an intermediate level representation which has been proven to be very powerful for addressing scene categorization problems. However, in most scene categorization methods, a scene is characterized by a single histogram based on the sole universal codebook, which is lack of enough discriminative ability to separate the similar images among different categories and results in low classification accuracy. In order to solve this problem, in this paper, we propose a novel scene categorization method that constructs class-specific codebooks based on feature selection strategy. Specifically, feature selection method mutual information is adopted to measure the visual word's contribution to each category and construct class-specific codebooks. Then, an image is characterized by a set of combined histograms (one histogram per category), each of which is generated by concentrating the traditional histogram based on universal codebook and the class-specific histogram grounded on class-specific codebook with an adaptive weighting coefficient. The improved combined-histogram provides useful information or cue to overcome the similarity of inter-class images. The proposed method is sufficiently evaluated over three wellknown scene classification datasets, and experimental results show that our proposed scene categorization method outperforms the state-of-the-art approaches.