Image Classification With Kernelized Spatial-Context
The goal of image classification is to classify a collection of unlabeled images into a set of semantic classes. Many methods have been proposed to approach this goal by leveraging visual appearances of local patches in images. However, the spatial context between these local patches also provides significant information to improve the classification accuracy. Traditional spatial contextual models, such as two-dimensional hidden Markov model, attempt to construct one common model for each image category to depict the spatial structures of the images in this class. However due to large intra-class variances in an image category, one single model has difficulties in representing various spatial contexts in different images. In contrast, we propose to construct a prototype set of spatial contextual models by leveraging the kernel methods rather than only one model. Such an algorithm combines the advantages of rich representation ability of spatial contextual models as well as the powerful classification ability of kernel method. In particular, we propose a new distance measure between different spatial contextual models by integrating joint appearance-spatial image features. Such a distance measure can be efficiently computed in a recursive formulation that scales well to image size. Extensive experiments demonstrate that the proposed approach significantly outperforms the state-of-the-art approaches.