In this paper, we propose a low-rank sparse coding (LRSC) method that exploits local
structure information among features in an image for the purpose of image-level …

Sparse coding can learn good robust representation to noise and model more higher-order
representation for image classification. However, the inference algorithm is computationally …

Abstract Sparse Representation-based Classification (SRC) is a powerful tool in
distinguishing signal categories which lie on different subspaces. Despite its wide …

In this paper, we propose a novel deep sparse coding network (SCN) capable of efficiently
adapting its own regularization parameters for a given application. The network is trained …

Bag-of-words document representations are often used in text, image and video processing.
While it is relatively easy to determine a suitable word dictionary for text documents, there is …

Classic sparse representation for classification (SRC) method fails to incorporate the label
information of training images, and meanwhile has a poor scalability due to the expensive …

Recent research has shown the initial success of sparse coding (Sc) in solving many
computer vision tasks. Motivated by the fact that kernel trick can capture the nonlinear …

Discriminative sparse coding has emerged as a promising technique in image analysis and
recognition, which couples the process of classifier training and the process of dictionary …

Sparse coding which encodes the original signal in a sparse signal space, has shown its
state-of-the-art performance in the visual codebook generation and feature quantization …

Recently, the sparse coding based codebook learning and local feature encoding have
been widely used for image classification. The sparse coding model actually assumes the …