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

This chapter reviews most popular texture analysis approaches under novel comparison
axes that are specific to biomedical imaging. A concise checklist is proposed as a user guide …

In this paper, we propose supervised dictionary learning (SDL) by incorporating information
on class labels into the learning of the dictionary. To this end, we propose to learn the …

Recently, a supervised dictionary learning (SDL) approach based on the Hilbert-Schmidt
independence criterion (HSIC) has been proposed that learns the dictionary and the …

Effective and efficient texture feature extraction and classification is an important problem in
image understanding and recognition. Recently, texton learning based texture classification …

Indoor localization is a challenging task as the signal propagation in indoor environments
does not adhere to the classical path loss or other simple models. Modern high-accuracy …

This chapter aims to provide an overview of the foundations of texture processing for
biomedical image analysis. Its purpose is to define precisely what biomedical texture is, how …

This chapter clarifies the important aspects of biomedical texture analysis under the general
framework introduced in Chapter 1. It was proposed that any approach can be characterized …

We propose a novel, lightweight supervised dictionary learning framework for text
classification based on data compression and representation. This two-phase algorithm …

We present a probabilistic modeling and inference framework for discriminative analysis
dictionary learning under a weak supervision setting. Dictionary learning approaches have …