Convolutional neural networks (CNNs) have been applied to visual tasks since the late
1980s. However, despite a few scattered applications, they were dormant until the mid …

Techniques from sparse signal representation are beginning to see significant impact in
computer vision, often on nontraditional applications where the goal is not just to obtain a …

Many real-world problems deal with collections of high-dimensional data, such as images,
videos, text, and web documents, DNA microarray data, and more. Often, such high …

In many recent object recognition systems, feature extraction stages are generally
composed of a filter bank, a non-linear transformation, and some sort of feature pooling …

Recently SVMs using spatial pyramid matching (SPM) kernel have been highly successful in
image classification. Despite its popularity, these nonlinear SVMs have a complexity O (n …

Sparse coding—that is, modelling data vectors as sparse linear combinations of basis
elements—is widely used in machine learning, neuroscience, signal processing, and …

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 …

Multiple instance learning (MIL) is a form of weakly supervised learning where training
instances are arranged in sets, called bags, and a label is provided for the entire bag. This …

In a sparse-representation-based face recognition scheme, the desired dictionary should
have good representational power (ie, being able to span the subspace of all faces) while …

Pedestrian detection is a problem of considerable practical interest. Adding to the list of
successful applications of deep learning methods to vision, we report state-of-theart and …