Results 1  10
of
162
Sparse Representation or Collaborative Representation: Which Helps Face Recognition?
"... As a recently proposed technique, sparse representation based classification (SRC) has been widely used for face recognition (FR). SRC first codes a testing sample as a sparse linear combination of all the training samples, and then classifies the testing sample by evaluating which class leads to th ..."
Abstract

Cited by 106 (17 self)
 Add to MetaCart
As a recently proposed technique, sparse representation based classification (SRC) has been widely used for face recognition (FR). SRC first codes a testing sample as a sparse linear combination of all the training samples, and then classifies the testing sample by evaluating which class leads to the minimum representation error. While the importance of sparsity is much emphasized in SRC and many related works, the use of collaborative representation (CR) in SRC is ignored by most literature. However, is it really the l1norm sparsity that improves the FR accuracy? This paper devotes to analyze the working mechanism of SRC, and indicates that it is the CR but not the l1norm sparsity that makes SRC powerful for face classification. Consequently, we propose a very simple yet much more efficient face classification scheme, namely CR based classification with regularized least square (CRC_RLS). The extensive experiments clearly show that CRC_RLS has very competitive classification results, while it has significantly less complexity than SRC.
Structured compressed sensing: From theory to applications
 IEEE TRANS. SIGNAL PROCESS
, 2011
"... Compressed sensing (CS) is an emerging field that has attracted considerable research interest over the past few years. Previous review articles in CS limit their scope to standard discretetodiscrete measurement architectures using matrices of randomized nature and signal models based on standard ..."
Abstract

Cited by 98 (15 self)
 Add to MetaCart
(Show Context)
Compressed sensing (CS) is an emerging field that has attracted considerable research interest over the past few years. Previous review articles in CS limit their scope to standard discretetodiscrete measurement architectures using matrices of randomized nature and signal models based on standard sparsity. In recent years, CS has worked its way into several new application areas. This, in turn, necessitates a fresh look on many of the basics of CS. The random matrix measurement operator must be replaced by more structured sensing architectures that correspond to the characteristics of feasible acquisition hardware. The standard sparsity prior has to be extended to include a much richer class of signals and to encode broader data models, including continuoustime signals. In our overview, the theme is exploiting signal and measurement structure in compressive sensing. The prime focus is bridging theory and practice; that is, to pinpoint the potential of structured CS strategies to emerge from the math to the hardware. Our summary highlights new directions as well as relations to more traditional CS, with the hope of serving both as a review to practitioners wanting to join this emerging field, and as a reference for researchers that attempts to put some of the existing ideas in perspective of practical applications.
Fisher Discrimination Dictionary Learning for Sparse Representation
"... 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 dictionary learning (DL) method to improve the pattern classification performance. Based on the Fisher discrimi ..."
Abstract

Cited by 73 (9 self)
 Add to MetaCart
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 dictionary learning (DL) method to improve the pattern classification performance. Based on the Fisher discrimination criterion, a structured dictionary, whose dictionary atoms have correspondence to the class labels, is learned so that the reconstruction error after sparse coding can be used for pattern classification. Meanwhile, the Fisher discrimination criterion is imposed on the coding coefficients so that they have small withinclass scatter but big betweenclass scatter. A new classification scheme associated with the proposed Fisher discrimination DL (FDDL) method is then presented by using both the discriminative information in the reconstruction error and sparse coding coefficients. The proposed FDDL is extensively evaluated on benchmark image databases in comparison with existing sparse representation and DL based classification methods.
Image deblurring and superresolution by adaptive sparse domain selection and adaptive regularization
 IEEE Trans. Image Process
, 2011
"... Abstract—As a powerful statistical image modeling technique, sparse representation has been successfully used in various image restoration applications. The success of sparse representation owes to the development of thenorm optimization techniques and the fact that natural images are intrinsically ..."
Abstract

Cited by 56 (11 self)
 Add to MetaCart
(Show Context)
Abstract—As a powerful statistical image modeling technique, sparse representation has been successfully used in various image restoration applications. The success of sparse representation owes to the development of thenorm optimization techniques and the fact that natural images are intrinsically sparse in some domains. The image restoration quality largely depends on whether the employed sparse domain can represent well the underlying image. Considering that the contents can vary significantly across different images or different patches in a single image, we propose to learn various sets of bases from a precollected dataset of example image patches, and then, for a given patch to be processed, one set of bases are adaptively selected to characterize the local sparse domain. We further introduce two adaptive regularization terms into the sparse representation framework. First, a set of autoregressive (AR) models are learned from the dataset of example image patches. The best fitted AR models to a given patch are adaptively selected to regularize the image local structures. Second, the image nonlocal selfsimilarity is introduced as another regularization term. In addition, the sparsity regularization parameter is adaptively estimated for better image restoration performance. Extensive experiments on image deblurring and superresolution validate that by using adaptive sparse domain selection and adaptive regularization, the proposed method achieves much better results than many stateoftheart algorithms in terms of both PSNR and visual perception. Index Terms—Deblurring, image restoration (IR), regularization, sparse representation, superresolution. I.
Spectral Compressive Sensing
"... Compressive sensing (CS) is a new approach to simultaneous sensing and compression of sparse and compressible signals. A great many applications feature smooth or modulated signals that can be modeled as a linear combination of a small number of sinusoids; such signals are sparse in the frequency do ..."
Abstract

Cited by 37 (5 self)
 Add to MetaCart
Compressive sensing (CS) is a new approach to simultaneous sensing and compression of sparse and compressible signals. A great many applications feature smooth or modulated signals that can be modeled as a linear combination of a small number of sinusoids; such signals are sparse in the frequency domain. In practical applications, the standard frequency domain signal representation is the discrete Fourier transform (DFT). Unfortunately, the DFT coefficients of a frequencysparse signal are themselves sparse only in the contrived case where the sinusoid frequencies are integer multiples of the DFT’s fundamental frequency. As a result, practical DFTbased CS acquisition and recovery of smooth signals does not perform nearly as well as one might expect. In this paper, we develop a new spectral compressive sensing (SCS) theory for general frequencysparse signals. The key ingredients are an oversampled DFT frame, a signal model that inhibits closely spaced sinusoids, and classical sinusoid parameter estimation algorithms from the field of spectrum estimation. Using peridogram and eigenanalysis based spectrum estimates (e.g., MUSIC), our new SCS algorithms significantly outperform the current stateoftheart CS algorithms while providing provable bounds on the number of measurements required for stable recovery. I.
Sparsity constrained nonlinear optimization: Optimality conditions and algorithms, arXiv preprint arXiv:1203.4580
, 2012
"... This paper treats the problem of minimizing a general continuously differentiable function subject to sparsity constraints. We present and analyze several different optimality criteria which are based on the notions of stationarity and coordinatewise optimality. These conditions are then used to de ..."
Abstract

Cited by 31 (8 self)
 Add to MetaCart
(Show Context)
This paper treats the problem of minimizing a general continuously differentiable function subject to sparsity constraints. We present and analyze several different optimality criteria which are based on the notions of stationarity and coordinatewise optimality. These conditions are then used to derive three numerical algorithms aimed at finding points satisfying the resulting optimality criteria: the iterative hard thresholding method and the greedy and partial sparsesimplex methods. The first algorithm is essentially a gradient projection method while the remaining two algorithms are of coordinate descent type. The theoretical convergence of these methods and their relations to the derived optimality conditions are studied. The algorithms and results are illustrated by several numerical examples. 1
Asymptotic analysis of complex LASSO via complex approximate message passing
 IEEE Trans. Inf. Theory
, 2011
"... Recovering a sparse signal from an undersampled set of random linear measurements is the main problem of interest in compressed sensing. In this paper, we consider the case where both the signal and the measurements are complexvalued. We study the popular reconstruction method of ℓ1regularized lea ..."
Abstract

Cited by 27 (9 self)
 Add to MetaCart
(Show Context)
Recovering a sparse signal from an undersampled set of random linear measurements is the main problem of interest in compressed sensing. In this paper, we consider the case where both the signal and the measurements are complexvalued. We study the popular reconstruction method of ℓ1regularized least squares or LASSO. While several studies have shown that the LASSO algorithm offers desirable solutions under certain conditions, the precise asymptotic performance of this algorithm in the complex setting is not yet known. In this paper, we extend the approximate message passing (AMP) algorithm to the complexvalued signals and measurements to obtain the complex approximate message passing algorithm (CAMP). We then generalize the state evolution framework recently introduced for the analysis of AMP, to the complex setting. Using the state evolution, we derive accurate formulas for the phase transition and noise sensitivity of both LASSO and CAMP. Our results are theoretically proved for the case of i.i.d. Gaussian sensing matrices. But we confirm through simulations that our results hold for larger class of random matrices. 1
Phase transitions for greedy sparse approximation algorithms
, 2009
"... A major enterprise in compressed sensing and sparse approximation is the design and analysis of computationally tractable algorithms for recovering sparse, exact or approximate, solutions of underdetermined linear systems of equations. Many such algorithms have now been proven using the ubiquitous R ..."
Abstract

Cited by 26 (12 self)
 Add to MetaCart
A major enterprise in compressed sensing and sparse approximation is the design and analysis of computationally tractable algorithms for recovering sparse, exact or approximate, solutions of underdetermined linear systems of equations. Many such algorithms have now been proven using the ubiquitous Restricted Isometry Property (RIP) [9] to have optimalorder uniform recovery guarantees. However, it is unclear when the RIPbased sufficient conditions on the algorithm are satisfied. We present a framework in which this task can be achieved; translating these conditions for Gaussian measurement matrices into requirements on the signal’s sparsity level, size and number of measurements. We illustrate this approach on three of the stateoftheart greedy algorithms: CoSaMP [27], Subspace Pursuit (SP) [11] and Iterated Hard Thresholding (IHT) [6]. Designed to allow a direct comparison of existing theory, our framework implies that IHT, the lowest of the three in computational cost, also requires fewer compressed sensing measurements than CoSaMP and SP.
A Note on Complexity of Lp Minimization
, 2010
"... We show that the Lp (0 ≤ p < 1) minimization problem arising from sparse solution construction and compressed sensing is both hard and easy. More precisely, for any fixed 0 < p < 1, we prove that checking the global minimal value of the problem is NPHard; but computing a local minimizer of ..."
Abstract

Cited by 25 (3 self)
 Add to MetaCart
We show that the Lp (0 ≤ p < 1) minimization problem arising from sparse solution construction and compressed sensing is both hard and easy. More precisely, for any fixed 0 < p < 1, we prove that checking the global minimal value of the problem is NPHard; but computing a local minimizer of the problem is polynomialtime doable. We also develop an interiorpoint algorithm with a provable complexity bound and demonstrate preliminary computational results of effectiveness of the algorithm.
Nonlocally Centralized Sparse Representation for Image Restoration
, 2011
"... The sparse representation models code an image patch as a linear combination of a few atoms chosen out from an overcomplete dictionary, and they have shown promising results in various image restoration applications. However, due to the degradation of the observed image (e.g., noisy, blurred and/o ..."
Abstract

Cited by 25 (8 self)
 Add to MetaCart
(Show Context)
The sparse representation models code an image patch as a linear combination of a few atoms chosen out from an overcomplete dictionary, and they have shown promising results in various image restoration applications. However, due to the degradation of the observed image (e.g., noisy, blurred and/or downsampled), the sparse representations by conventional models may not be accurate enough for a faithful reconstruction of the original image. To improve the performance of sparse representation based image restoration, in this paper the concept of sparse coding noise is introduced, and the goal of image restoration turns to how to suppress the sparse coding noise. To this end, we exploit the image nonlocal selfsimilarity to obtain good estimates of the sparse coding coefficients of the original image, and then centralize the sparse coding coefficients of the observed image to those estimates. The socalled nonlocally centralized sparse representation (NCSR) model is as simple as the standard sparse representation model, while our extensive experiments on various types of image restoration problems, including denoising, deblurring and superresolution, validate the generality and stateoftheart performance of the proposed NCSR algorithm.