Convolutional sparse coding (CSC) improves sparse coding by learning a shift-invariant dictionary from the data. However, existing CSC algorithms operate in the batch mode and are expensive, in terms of both space and time, on large data sets. In this paper, we alleviate these problems by using online learning. The key is a reformulation of the CSC objective so that convolution can be handled e...

Spoken language recognition requires a series of signal processing steps and learning algorithms to model distinguishing characteristics of different languages. In this paper, we present a sparse discriminative feature learning framework for language recognition. We use sparse coding, an unsupervised method, to compute efficient representations for spectral features from a speech utterance whil...

Sparse coding is a crucial subroutine in algorithms for various signal processing, deep learning, and other machine learning applications. The central goal is to learn an overcomplete dictionary that can sparsely represent a given dataset. However, storage, transmission, and processing of the learned dictionary can be untenably high if the data dimension is high. In this paper, we consider the ...

Sparse coding represents a signal sparsely by using an overcomplete dictionary, and obtains promising performance in practical computer vision applications, especially for signal restoration tasks such as image denoising and image inpainting. In recent years, many discriminative sparse coding algorithms have been developed for classification problems, but they cannot naturally handle visual dat...

The issue of visual discomfort has long been restricting the develop‐ ment of advanced stereoscopic 3D video technology. Bolstered by the require‐ ment of highly comfortable three-dimensional (3D) content service, predicting the degree of visual comfort automatically with high accuracy has become a topic of intense study. This paper presents a novel visual comfort assessment (VCA) metric based ...

The Bag-of-visual-words (BOVW) model discards image spatial information, and the computing cost is expensive on spatial pyramid matching(SPM) model. Due to sparse coding approach exhibit super performance in information retrieval, hence, we propose a new sparse coding image retrieval algorithm. Using 2 l norm replace 0 l norm in SPM vector quantization. The local information was incorporated in...

BACKGROUND Ultrasound is considered a reliable, widely available, non-invasive, and inexpensive imaging technique for assessing and detecting the development phases of cancer; both in vivo and ex vivo, and for understanding the effects on cell cycle and viability after ultrasound treatment. METHODS Based on the topological continuity characteristics, and that adjacent points or areas represen...

1 Olfactory coding: Unusual conductances contribute to sparse neural 2 representations. 3 4 Focus on “Intrinsic Membrane Properties and Inhibitory Synaptic Input of 5 Kenyon Cells as Mechanisms for Sparse Coding?” 6 7 8 Rose C. Ong and Mark Stopfer 9 10 National Institute of Child Health and Human Development, US National Institutes 11 of Health, Bethesda, Maryland; and Department of Biochemist...

An r-identifying code on a graph G is a set C ⊂ V (G) such that for every vertex in V (G), the intersection of the radius-r closed neighborhood with C is nonempty and different. Here, we provide an overview on codes for the n-dimensional lattice, discussing the case of 1-identifying codes, constructing a sparse code for the 4-dimensional lattice as well as showing that for fixed n, the minimum ...

Sparse coding performs well in image classification. However, robust target recognition requires a lot of comprehensive template images and the sparse learning process is complex. We incorporate sparsity into a template matching concept to construct a local sparse structure matching (LSSM) model for general infrared target recognition. A local structure preserving sparse coding (LSPSc) formulat...