Spatial Graph for Image Classification

Spatial information in images is considered to be of great importance in the process of object recognition. Recent studies show that human's classification accuracy might drop dramatically if the spatial information of an image is removed. The original bag-of-words (BoW) model is actually a system simulating such a classification process with incomplete information. To handle the spatial information, spatial pyramid matching (SPM) was proposed, which has become the most widely used scheme in the purpose of spatial modeling. Given an image, SPM divides it into a series of spatial blocks on several levels and concatenates the representations obtained separately within all the blocks. SPM greatly improves the performance since it embeds spatial information into BoW. However, SPM ignores the relationships between the spatial blocks. To address this problems, we propose a new scheme based on a spatial graph, whose nodes correspond to the spatial blocks in SPM, and edges correspond to the relationships between the blocks. Thorough experiments on several popular datasets verify the advantages of the proposed scheme.