Multiple spatial pooling for visual object recognition

Global spatial structure is an important factor for visual object recognition but has not attracted sufficient attention in recent studies. Especially, the problems of features' ambiguity and sensitivity to location change in the image space are not yet well solved. In this paper, we propose multiple spatial pooling (MSP) to address these problems. MSP models global spatial structure with multiple Gaussian distributions and then pools features according to the relations between features and Gaussian distributions. Such a process is further generalized into a unified framework, which formulates multiple pooling using matrix operation with structured sparsity. Experiments in terms of scene classification and object categorization demonstrate that MSP can enhance traditional algorithms with small extra computational cost.