摘要稀疏表示模型常利用训练样本学习过完备字典, 旨在获得信号的冗余稀疏表示. 设计简单,
高效, 通用性强的字典学习算法是目前的主要研究方向之一, 也是信息领域的研究热点 …

Sparse representation has attracted much attention from researchers in fields of signal
processing, image processing, computer vision, and pattern recognition. Sparse …

In pattern recognition and computer vision, one is often faced with scenarios where the
training data used to learn a model have different distribution from the data on which the …

Person re-identification has been widely studied due to its importance in surveillance and
forensics applications. In practice, gallery images are high-resolution (HR) while probe …

This paper introduces a new approach to address the person re-identification problem in
cameras with non-overlapping fields of view. Unlike previous approaches that learn …

Dictionary learning and sparse representation (DLSR) is a recent and successful
mathematical model for data representation that achieves state-of-the-art performance in …

There have been several recent studies that used sparse representation for fMRI signal
analysis and activation detection based on the assumption that each voxel's fMRI signal is …

In histopathological image analysis, feature extraction for classification is a challenging task
due to the diversity of histology features suitable for each problem as well as presence of …

Dictionary learning algorithms have been successfully used for both reconstructive and
discriminative tasks, where an input signal is represented with a sparse linear combination …

Recognition of natural emotions from human faces is an interesting topic with a wide range
of potential applications, such as human-computer interaction, automated tutoring systems …