Improved spatial pyramid matching for scene recognition

A scene image is typically composed of successive background contexts and objects with regular shapes. To acquire such spatial information, we propose a new type of spatial partitioning scheme and a modified pyramid matching kernel based on spatial pyramid matching (SPM). A dense histogram of oriented gradients (HOG) is used as a low-level visual descriptor. Furthermore, inspired by the expressive coding ability of autoencoders, we also propose another approach that encodes local descriptors into mid-level features using various autoencoders. The learned mid-level features are encouraged to be sparse, robust and contractive. Then, modified spatial pyramid pooling and local normalization of the mid-level features facilitate the generation of high-level image signatures for scene classification. Comprehensive experimental results on publicly available scene datasets demonstrate the effectiveness of our methods.