Learning an Orthogonal and Smooth Subspace for Image Classification

The recent years have witnessed a surge of interests of learning a subspace for image classification, which has aroused considerable researches from the pattern recognition and signal processing fields. However, for image classification, the accuracies of previous methods are not so high since they neglect some particular characters of the image data. In this paper, we propose a new subspace learning method. It constrains that the transformation basis is orthonormal and the derived coefficients are spatially smooth. Classification is then performed in the image subspace. The proposed method can not only represent the intrinsic structure of the image data, but also avoid over-fitting. More importantly, it can be considered as a general framework, within which the performances of other subspace learning methods can be improved in the same way. Some related analyses of the proposed approach are presented. Promising experimental results on different kinds of real images demonstrate the effectiveness of our algorithm for image classification.