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 …

Sparse representation-based classification (SRC) has been developed and shown great
potential for real-world application. Based on SRC, Yang et al. devised an SRC steered …

We present a novel dictionary learning (DL) approach for sparse representation based
classification in kernel feature space. These sparse representations are obtained using …

Sparse representation based classification (SRC) has received much attention in computer
vision and pattern recognition. SRC codes a testing sample by sparse linear combination of …

In complex visual recognition tasks, it is typical to adopt multiple descriptors, which describe
different aspects of the images, for obtaining an improved recognition performance …

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 …

In this paper, we propose class-discriminative kernel sparse representation-based
classification (KSRC) using multi-objective optimization (MOO) called KSRC 2.0. In sparse …

Among all image representation and classification methods, sparse representation has
proven to be an extremely powerful tool. However, a limited number of training samples are …

Recently, sparse representation-based classification (SRC) has been widely studied and
has produced state-of-the-art results in various classification tasks. Learning useful and …

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 …