Many efforts have been devoted to develop alternative methods to traditional vector
quantization in image domain such as sparse coding and soft-assignment. These …

Dans cette thèse, nous étudions les méthodes d'approximation et d'apprentissage qui
fournissent des représentations parcimonieuses. Ces méthodes permettent d'analyser des …

This paper proposes a novel local sparse representation for driver drowsiness expression
recognition. A discriminative local structured dictionary is learned from the non-overlapping …

This paper proposes a novel Dictionary Learning (DL) algorithm for pattern classification
tasks. Based on the Distance Dependent Indian Buffet Process (DDIBP) model, a shared …

In recent years, Sparse Representation (SR) and Dictionary Learning (DL) have emerged as
powerful tools for efficiently processing of image and video data in non-traditional ways. An …

Based on sparse representation, a robust global and local classification algorithm for visual
target tracking in uncertain environment was proposed in this paper. The global region of …

The main finding of this work is that the standard image classification pipeline, which
consists of dictionary learning, feature encoding, spatial pyramid pooling and linear …

Dimensionality reduction via manifold learning offers an elegant representation of data
whereby the high dimensional feature space is parameterized by a lower dimensional space …

Abstract In recent years, Sparse Representation (SR) and Dictionary Learning (DL) have
emerged as powerful tools for efficient processing image and video data in non-traditional …