Indoor scene recognition based on deep learning and sparse representation

Abstract

Indoor scene images have the characteristics of small inter-class variety and large intra-class variety because of the content complexity of indoor scene images and the influence of illumination change and partial occlusion. This makes it difficult to effectively represent the semantic information of the indoor scene using traditional shallow feature learning. We present a comprehensive method combining deep features and sparse representation for indoor scene recognition in this paper. In terms of feature extraction, a Faster R-CNN based multi-class detector is training for extracting object information to be as the low-level features. An improved bag-of-words model is designed to build mid-level features from object-based low-level features, which retain the spatial information of low-level features. For improving the robustness of the proposed method, sparse representation is used to make the final decision of indoor scene recognition from mid-level features. Experimental results on indoor scene subset of MIT-67 dataset show that our proposed method can achieve a superior performance in comparison to baseline methods.