This paper summarizes the top state-of-the-art contributions reported on the MNIST dataset
for handwritten digit recognition. This dataset has been extensively used to validate novel …

Image representations, from SIFT and Bag of Visual Words to Convolutional Neural
Networks (CNNs), are a crucial component of almost any image understanding system …

Image representations, from SIFT and bag of visual words to convolutional neural networks
(CNNs) are a crucial component of almost all computer vision systems. However, our …

A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse
coding is presented. In addition to using class labels of training data, we also associate label …

Despite the importance of image representations such as histograms of oriented gradients
and deep Convolutional Neural Networks (CNN), our theoretical understanding of them …

When applying sparse representation techniques to images, the standard approach is to
independently compute the representations for a set of overlapping image patches. This …

Deep learning methods for image classification and object detection are overviewed. In
particular we consider such deep models as autoencoders, restricted Boltzmann machines …

In this paper, we propose a novel coupled dictionary training method for single-image super-
resolution (SR) based on patchwise sparse recovery, where the learned couple dictionaries …

The employed dictionary plays an important role in sparse representation or sparse coding
based image reconstruction and classification, while learning dictionaries from the training …

Sparse representation based classification has led to interesting image recognition results,
while the dictionary used for sparse coding plays a key role in it. This paper presents a novel …