Sparse representation (SR) has been successfully used in the classification of hyperspectral
images (HSIs) by representing HSI pixels over a dictionary and yielding discriminative …

In this paper, we propose a novel low-rank double dictionary learning (LRD 2 L) method for
robust image classification tasks, in which the training and testing samples are both …

This paper proposes a new framework of dictionary learning for a recently developed study,
sparse representation of monogenic signal. The proposed framework is applied to target …

Dictionary and deep learning algorithms facilitate efficient signal representations, thereby
offering tremendous representational power along with achieving good recognition rates in …

Deep Convolutional Neural Networks (CNN) enforce supervised information only at the
output layer, and hidden layers are trained by back propagating the prediction error from the …

This paper presents an innovative sparse representation model using the complete marginal
Fisher analysis (CMFA) framework for different challenging visual recognition tasks. First, a …

In this paper, we propose a low-rank representation method that incorporates graph
regularization for robust subspace clustering. We make the assumption that high …

In this paper, we propose a new discriminative non-linear dictionary learning approach,
called correlation constrained structured kernel KSVD, for object recognition. The objective …

We propose a method that obtains a discriminative visual dictionary and a nonlinear
classifier for visual tracking tasks in a sparse coding manner based on the globally linear …

In recent years, sparse representation-based classification (SRC) is one of the most
successful methods and has been shown impressive performance in various classification …