Learning weighted geometric pooling for image classification
Local feature extraction, coding, spatial pooling, and image classification are the four typical steps for state-of-the-art visual recognition systems. Unlike previous work that treats spatial pooling and image classification as separated steps, we propose to jointly learn the geometric pooling and image classifier such that class-specific geometric information of local descriptors can be incorporated to improve classification performance. Inspired by previous work of spatial pyramid matching and receptive field learning, we also propose spatial pyramid geometric pooling, receptive field geometric pooling and random partition geometric pooling approaches to further exploit the spatial structural information to boost classification performance. Experiments on 15-scene dataset validate the advantages of our proposed algorithms.