Dictionary learning has been used to solve inverse problems in imaging and as an
unsupervised feature extraction tool in vision. The main disadvantage of dictionary learning …

A complete and discriminative dictionary can achieve superior performance. However, it
also consumes extra processing time and memory, especially for large datasets. Most …

In this work, we tackle the problem of multi-label classification using a sparsity-based
approach. Multi-label classification problems, in which each instance is associated with a set …

This paper concerns the development of locality-preserving methods for object recognition.
The major purpose is consideration of both descriptor-level locality and image-level locality …

This paper investigates information services in vehicular networks via cooperative
infrastructure-to-vehicle (I2V), vehicle-to-vehicle (V2V) communications. In particular, we …

In this paper, a novel semi-supervised dictionary learning and sparse representation (SS-
DLSR) is proposed. The proposed method benefits from the supervisory information by …

In this paper, we propose a novel Fisher discriminant unsupervised dictionary learning (FD-
UDL) approach, for improving the clustering performance of state-of-the-art dictionary …

Visual classification of facial pose is desirable for computer vision applications such as face
recognition, human computer interaction, and affective computing. However, accurate …

Dictionary learning (DL) based block compressive sensing (BCS) aims to obtain both good
sparse representation and reconstructed image with high precision. Traditional methods …

Automatic face recognition has received significant performance improvement by
developing specialized facial image representations. On the other hand, spatial pyramid …