Abstract: In order to improve the recognition accuracy of indoor scene images a method was proposed through fusing the global and local features. First, the SIFT feature of scene images are extracted and the key points of SIFT are clustered in order to obtain the same dimension feature vector. The PCA is employed to reduce the dimension of feature matrix. Second, the PHOG and Gist features are extracted respectively and fused with the SIFT to construct feature matrix. Finally, the SVM is employed to classify the scene images' types. The experimental results show that the recognition accuracy of multi-featured fusion is better than those single-featured ones.

Key words: indoor; scene recognition; single feature; multifeatures fusing