Recently, sparse coding has become popular for image classification. However, images are often captured under different conditions such as varied poses, scales and different camera parameters. This means local features may not be discriminative enough to cope with these variations. To solve this problem, affine transformation along with sparse coding is proposed. Although proven effective, the ...

Recent advances suggest that a wide range of computer vision problems can be addressed more appropriately by considering non-Euclidean geometry. This paper tackles the problem of sparse coding and dictionary learning in the space of symmetric positive definite matrices, which form a Riemannian manifold. With the aid of the recently introduced Stein kernel (related to a symmetric version of Breg...

Inspired by recent work on convex formulations of clustering (Lashkari & Golland, 2008; Nowozin & Bakir, 2008) we investigate a new formulation of the Sparse Coding Problem (Olshausen & Field, 1997). In sparse coding we attempt to simultaneously represent a sequence of data-vectors sparsely (i.e. sparse approximation (Tropp et al., 2006)) in terms of a “code” defined by a set of basis elements,...

Sparse coding has been a popular learning model in machine learning field. However, due to the complexity of the learning model, the high computational cost has seriously hindered its application. Toward this purpose, this paper presents a parallel sparse coding method to improve the performance by exploiting the power of acceleration technologies such as Intel MIC and GPU. We use both parallel...

Sparse coding which encodes the original signal in a sparse signal space, has shown its state-of-the-art performance in the visual codebook generation and feature quantization process of BoW based image representation. However, in the feature quantization process of sparse coding, some similar local features may be quantized into different visual words of the codebook due to the sensitiveness o...

The goal in sparse coding is to seek a linear basis representation where each image is represented by a small number of active coefficients. The learning algorithm involves adapting a basis vector set while imposing a low-entropy, or sparse, prior on the output coefficients. Sparse coding applied on natural images has been shown to extract wavelet-like structure [9, 4]. However, our experience ...

We propose in this paper a novel sparse subspace clustering method that regularizes sparse subspace representation by exploiting the structural sharing between tasks and data points via group sparse coding. We derive simple, provably convergent, and computationally efficient algorithms for solving the proposed group formulations. We demonstrate the advantage of the framework on three challengin...

This paper investigates a mechanism for reliable generation of sparse code in a sparsely connected, hierarchical, learning memory. Activity reduction is accomplished with local competitions that suppress activities of unselected neurons so that costly global competition is avoided. The learning ability and the memory characteristics of the proposed winner-take-all network and an oligarchy-take-...

We present two new methods which extend the traditional sparse coding approach with supervised components. The goal of these extensions is to increase the suitability of the learned features for classification tasks while keeping most of their general representation performance. A special visualization is introduced which allows to show the principal effect of the new methods. Furthermore some ...

A number of researchers have theorized that the brain may be employing some form of hierarchical model of features in visual processing. Nodes at the bottom of the hierarchy would represent local, spacially-oriented, specific features, while levels further up the hierarchy would detect increasingly complex, spatially-diffuse, and invariant features, with nodes in the uppermost layers correspond...