A new deep representation for large-scale scene classification.

Large scale scene classification based on image is an important problem in computer vision. In this paper, we propose a method to fuse the local features of scene images into a geometric feature that can reflect both the geometric features of the scene image and the color intensity distribution. First, each scene image is segmented into a set of individually connected regions according to their color intensity distribution. A region adjacency graph is constructed to encode the geometric properties and color intensity of scene images. Later, a 5 tier CNN architecture was constructed to study regional features. Then, a thinning process is carried out to obtain a discriminant and compact template set from the training rag. These templates are used to extract graphlets finished bag (r-bogs) images represented by each scene. Finally, the strategy of boosting development is to classify the extracted r-bogs scenes. Experimental results on different datasets demonstrate the effectiveness and effectiveness of the proposed method.