Orthogonal Projective Sparse Coding for image representation.

We consider the problem of image representation for visual analysis. When representing images as vectors, the feature space is of very high dimensionality, which makes it difficult for applying statistical techniques for visual analysis. One then hope to apply matrix factorization techniques, such as Singular Vector Decomposition (SVD) to learn the low dimensional hidden concept space. Among various matrix factorization techniques, sparse coding receives considerable interests in recent years because its sparse representation leads to an elegant interpretation. However, most of the existing sparse coding algorithms are computational expensive since they compute the basis vectors and the representations iteratively. In this paper, we propose a novel method, called Orthogonal Projective Sparse Coding (OPSC), for efficient and effective image representation and analysis. Integrating the techniques from manifold learning and sparse coding, OPSC provides a sparse representation which can capture the intrinsic geometric structure of the image space. Extensive experimental results on real world applications demonstrate the effectiveness and efficiency of the proposed approach.