The employed dictionary plays an important role in sparse representation or sparse coding
based image reconstruction and classification, while learning dictionaries from the training …

Discriminative dictionary learning aims to learn a dictionary from training samples to
enhance the discriminative capability of their coding vectors. Several discrimination terms …

In recent years, sparse representation has been widely used in object recognition
applications. How to learn the dictionary is a key issue to sparse representation. A popular …

Sparse representation based classification has led to interesting image recognition results,
while the dictionary used for sparse coding plays a key role in it. This paper presents a novel …

Dictionary learning (DL) for sparse coding has shown promising results in classification
tasks, while how to adaptively build the relationship between dictionary atoms and class …

Despite the fact that different objects possess distinct class-specific features, they also
usually share common patterns. This observation has been exploited partially in a recently …

Dictionary learning plays a crucial role in sparse representation based image classification.
In this paper, we propose a novel approach to learn a discriminative dictionary with low-rank …

Motivated by image reconstruction, sparse representation based classification (SRC) has
been shown to be an effective method for applications like face recognition. In this paper, we …

Empirically, we find that, despite the class-specific features owned by the objects appearing
in the images, the objects from different categories usually share some common patterns …

Both interclass variances and intraclass similarities are crucial for improving the
classification performance of discriminative dictionary learning (DDL) algorithms. However …