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 …

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

Data-driven sparse models have been shown to give superior performance for image
classification tasks. Most of these works depend on learning a synthesis dictionary and the …

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 …

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 …

In this paper, we propose a novel supervised hierarchical sparse coding model based on
local image descriptors for classification tasks. The supervised dictionary training is …

In this paper, we propose a multiple kernel learning (MKL) algorithm that is based on the
sparse representation-based classification (SRC) method. Taking advantage of the …

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 …

We propose a joint representation and classification framework that achieves the dual goal
of finding the most discriminative sparse overcomplete encoding and optimal classifier …

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 …