Integrating multiple different yet complementary feature representations has been proved to
be an effective way for boosting tracking performance. This paper investigates how to …

Visual tracking using multiple features has been proved as a robust approach because
features could complement each other. Since different types of variations such as …

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 …

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 …

By means of a sparse collaborative representation mechanism, sparse-representation-
based classifiers show a superior performance in hyperspectral image (HSI) classification …

In this letter, we address sparse signal recovery in a Bayesian framework where sparsity is
enforced on reconstruction coefficients via probabilistic priors. In particular, we focus on the …

Promising results have been achieved in image classification problems by exploiting the
discriminative power of sparse representations for classification (SRC). Recently, it has been …

Recent studies have demonstrated the advantages of fusing information from multiple views
for various machine learning applications. However, most existing approaches assumed the …

Sparse reconstruction has attracted considerable attention in recent years and shown
powerful capabilities in many applications. In standard sparse reconstruction, the sparse …