Locally linear embedded sparse coding for image representation

Abstract

Recently, sparse coding has been widely and successfully used in image classification, noise reduction, texture synthesis and audio processing. Although traditional sparse coding method with fixed dictionaries like wavelet and curvelet can produce promising results, unsupervised sparse coding has shown its advantage by optimizing the dictionary adaptively. However, existing unsupervised sparse coding failed to consider the high dimensional manifold information within data. Recently, a graph regularized sparse coding method has shown outstanding performance by incorporating graph laplacian manifold information. In this paper, we proposed a sparse coding method called locally linear embedded sparse coding, to consider the local manifold structure as well as learning the sparse representation. We also provided a novel modified online dictionary learning method which iteratively utilizes modified least angle regression and block coordinate descent method to solve the problem. Instead of getting entire coefficient matrix then update dictionary matrix, our method updates coefficient vector and dictionary matrix in each inner iteration. Extensive experimental results have demonstrated the efficiency and accuracy of our method in image clustering.