Linear Discriminative Learning for Image Classification

Learning discriminative spatial representation for image classification

Spatial Pyramid Representation (SPR) [7] introduces spatial layout information to the orderless bag-of-features (BoF) representation. SPR has become the standard and has been shown to perform competitively against more complex methods for incorporating spatial layout. In SPR the image is divided into regular grids. However, the grids are taken as uniform spatial partitions without any theoretic...

Design of Non-Linear Discriminative Dictionaries for Image Classification

In recent years there has been growing interest in designing dictionaries for image classification. These methods, however, neglect the fact that data of interest often has non-linear structure. Motivated by the fact that this non-linearity can be handled by the kernel trick, we propose learning of dictionaries in the high-dimensional feature space which are simultaneously reconstructive and di...

Prototype Discriminative Learning for Face Image Set Classification

Learning Discriminative Multilevel Structured Dictionaries for Supervised Image Classification

Sparse representations using overcomplete dictionaries have proved to be a powerful tool in many signal processing applications such as denoising, super-resolution, inpainting, compression or classification. The sparsity of the representation very much depends on how well the dictionary is adapted to the data at hand. In this paper, we propose a method for learning structured multilevel diction...

Discriminative Tensor Sparse Coding for Image Classification

A novel approach to learn a discriminative dictionary over a tensor sparse model is presented. A structural incoherence constraint between dictionary atoms from different classes is introduced to promote discriminating information into the dictionary. The incoherence term encourages dictionary atoms to be as independent as possible. In addition, we incorporate classification error into the obje...