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About 10,000 papers for “sparse representation”

Sparse representation for . . .

Thang V. Pham, Arnold W. M. Smeulders · 2006

This paper offers a sparse, multiscale representation of objects. It captures the object appearance by selection from a very large dictionary of Gaussian differential basis functions. The learning procedure results from the matching pursuit algorithm, while the recognition is based on polynomial approximation to the bases, turning image matching into a problem of polynomial evaluation. The method is suited for coarse recognition between objects and, by adding more bases, also for fine recognition of the object pose. The advantages over the common representation using PCA include storing sampled points for recognition is not required, adding new objects to an existing data set is trivial because retraining other object models is not needed, and significantly in the important case where one has to scan an image over multiple locations in search for an object, the new representation is readily available as opposed to PCA projection at each location. The experimental result on the COIL-100 data set demonstrates high recognition accuracy with real-time performance.

Cited by 0

Subspace-Sparse Representation

Chong You, Renè Vidal · arXiv (Cornell University) · 2015

Given an overcomplete dictionary $A$ and a signal $b$ that is a linear combination of a few linearly independent columns of $A$, classical sparse recovery theory deals with the problem of recovering the unique sparse representation $x$ such that $b = A x$. It is known that under certain conditions on $A$, $x$ can be recovered by the Basis Pursuit (BP) and the Orthogonal Matching Pursuit (OMP) algorithms. In this work, we consider the more general case where $b$ lies in a low-dimensional subspace spanned by some columns of $A$, which are possibly linearly dependent. In this case, the sparsest solution $x$ is generally not unique, and we study the problem that the representation $x$ identifies the subspace, i.e. the nonzero entries of $x$ correspond to dictionary atoms that are in the subspace. Such a representation $x$ is called subspace-sparse. We present sufficient conditions for guaranteeing subspace-sparse recovery, which have clear geometric interpretations and explain properties of subspace-sparse recovery. We also show that the sufficient conditions can be satisfied under a randomized model. Our results are applicable to the traditional sparse recovery problem and we get conditions for sparse recovery that are less restrictive than the canonical mutual coherent condition. We also use the results to analyze the sparse representation based classification (SRC) method, for which we get conditions to show its correctness.

Cited by 4Open access

Algorithms on Sparse Representation

D. Khalandar Basha, T. Venkateswarlu · International Journal of Engineering & Technology · 2018

Representation of signals and images in sparse become more interesting for various applications like restoration, compression and recognition. Many researches carried out in the era of sparse representation. Sparse represents signal or image as a few elements from the dictionary atoms. There are various algorithms proposed by researchers for learning dictionary. This paper discuss some of the terms related to sparse like regularization term, minimization, minimization, minimizationfollowed by the pursuit algorithms for solving problem, greedy algorithms and relaxation algorithms. This paper gives algorithmic approaches for the algorithms.

Cited by 0Open access

Autoencoders Learning Sparse Representation

Abhinav Sharma, Ruchir Gupta · 2022

Many regularized autoencoders learn a sparse rep-resentation of data. This type of representation enhances robust-ness against noise and computational efficiencies. Our objective in this paper is to provide the conditions under which sparsity is encouraged by AE under a little less restrictive view of data. We have shown a relaxed observed representation of input data and given the conditions on AE to promote sparsity.

Cited by 1

Sparse Representation based Fisher . . .

Meng Yang, Lei Zhang, Xiangchu Feng et al. · 2015

The employed dictionary plays an important role in sparse representation or sparse coding based image reconstruction and classification, while learning dictionaries from the training data has led to state-of-the-art results in image classification tasks. However, many dictionary learning models exploit only the discriminative information in either the representation coefficients or the representation residual, which limits their performance. In this paper we present a novel dictionary learning method based on the Fisher discrimination criterion. A structured dictionary, whose atoms have correspondences to the subject class labels, is learned, with which not only the representation residual can be used to distinguish different classes, but also the representation coefficients have small within-class scatter and big between-class scatter. The classification scheme associated with the proposed Fisher discrimination dictionary learning (FDDL) model is consequently presented by exploiting the discriminative information in both the representation residual and the representation coefficients. The proposed FDDL model is extensively evaluated on various image datasets, and it shows superior performance to many state-of-the-art dictionary learning methods in a variety of classification tasks.

Cited by 0

Sparse Representation of Signals

Mesárošová, Michaela · Brno University of Technology Digital Library (Brno University of Technology) · 2023

Imobilné či dlholežiace osoby sú rizikovou skupinou na vznik preležanín a zároveň si vyžadujú ďalšiu starostlivosť. Preto je potrebné stav stav takýchto osôb monitorovať a to čo najjednoduchšie a efektívne. V tejto práci sa zameriavame na spracovanie signálu poskytovaného tlakovým matracom so senzorovou mriežkou 30×11, na ktorom je osoba umiestnená a jeho možnosti využitia po prevedení do koeficientov riedkej reprezentácie. Redundantné slovníky, alebo tiež rámce, umožňujú neortogonálne reprezentovanie signálov, čo vedie práve k riedkemu zastúpeniu koeficientov. Keďže tento prístup poskytuje množstvo výhodných vlastností a je využívaný v rôznych aplikáciách, ako odšumovanie, segmentácia, robustné prevody, kvantová teória a ďalšie, overili sme možnosť klasifikovania pozície ľahu osoby na základe riedkej reprezentácie. Výsledky boli porovnávané s ďalšími tradičnými klasifikačnými metódami, ktoré sa svojou spoľahlivosťou preukázali ako menej vhodné pre klasifikačný problém s najlepším dosiahnutým výsledkom 92,41 % pre CNN, avšak s vysokými nárokmi na čas a návrh a zložitosť. Úspešnosť klasifikácie pomocou riedkej reprezentácie dosiahla 92,76 % s nižšími nárokmi na návrh a implementačnú komplexitu. Preskúmané boli tiež možnosti klasifikácie a rekonštrukcie obrazu obsahujúceho oklúzie, kde sa riedka reprezentácia ukázala ako efektívna metóda na odstránenie týchto porúch.

Cited by 0

Sparse Representation Using SVR

Abhijit S. Shinde, Sachin D. Ruikar, Ashwini Ashok Patil · 2017

This paper represents super resolution technique for higher level of scale. It describes categorization like if lower the resolution, the lesser details are captured and for higher resolution the image details are higher and with greater image quality. Image sensors (e.g. CCD) and optics, camera, lens are the parameter which limits the imagine system. In real applications to capture high resolution images using constructing imaging tools like optical chips, lens, and optical component is not practical due to its high cost. Where, it is all time acceptable to improve quality of images or video captured already as the legacy from the development of digital imaging. Key way to overcome the resolution problem is to use machine learning techniques to post-process the captured images i.e. Super-resolution (SR).

Cited by 0

Fast Kernel Sparse Representation

Hanxi Li, Yongsheng Gao, Jun Sun · 2011

Two efficient algorithms are proposed to seek the sparse representation on high-dimensional Hilbert space. By proving that all the calculations in Orthogonal Match Pursuit (OMP) are essentially inner-product combinations, we modify the OMP algorithm to apply the kernel-trick. The proposed Kernel OMP (KOMP) is much faster than the existing methods, and illustrates higher accuracy in some scenarios. Furthermore, inspired by the success of group-sparsity, we enforce a rigid group-sparsity constraint on KOMP which leads to a noniterative variation. The constrained cousin of KOMP, dubbed as Single-Step KOMP (S-KOMP), merely takes one step to achieve the sparse coefficients. A remarkable improvement (up to 2,750 times) in efficiency is reported for S-KOMP, with only a negligible loss of accuracy.

Cited by 17

Sparse representation of data

Thomas Villmann, Frank-Michael Schleif, Barbara Hammer et al. · 2010

Abstract. The amount of electronic data available today as well as its dimensionality and complexity increases rapidly in many scientific areas including biology, (bio-)chemistry, medicine, physics and its application fields like robotics, bioinformatics or multimedia technologies. Many of these data sets are very complex but have also a simple inherent structure which allows an appropriate sparse representation and modeling of such data with less or no information loss. Advanced methods are needed to extract these inherent buthiddeninformation. Sparsity can be observed at different levels: sparse representation of data points using e.g. dimensionality reduction for efficient data storage, sparse representation of full data sets using e.g. prototypes to achieve compact models for lifelong learning and sparse models of the underlying data structure using sparse encoding techniques. One main goal is to achieve a human-interpretable representation of the essential information. Sparse representations account for the ubiquitous problem that humans have to deal with ever increasing and inherently unlimited information by means of limited resources such as limited time, memory, or perception abilities. Starting with the seminal paper of Olshausen&Field [40] researchers recognized that sparsity can be used as a fundamental principle to arrive at very efficient information processing models for huge and complex data such as observed e.g. in the visual cortex. Nowadays, sparse models include diverse methods such as relevance learning in prototype based representations, sparse coding neural gas, factor analysis methods, latent semantic indexing, sparse Bayesian networks, relevancevectormachines andother. Thistutorialpaperreviews recent developments in the field. 1

Cited by 1

Sparse representation shape model

Yuelong Li, Jufu Feng · 2010

This paper introduces a novel shape model, Sparse Representation Shape Model (SRSM). Rather than for modeling specific deformable shapes, this model is specially designed for shape segmentation and matching. This model is utilized under the framework of Active Shape Models (ASM). Unlike the Linear Point Distribution Model utilized by original ASM, which relies on obscure statistical boundary to do shape regularization, SRSM distinctly distinguishes valid shape information and errors contained in input candidate shape from structure and by making use of sparse representation, SRSM could acquire the maximum valid shape information, and hence could achieve optimal shape regularization. Further-more, through exploiting the reliability information of each landmark, SRSM can be improved further to form Weighted SRSM, which is much more evident and accurate.

Cited by 10

Sparse Representation Based Projections

Radu Timofte, Luc Van Gool · 2011

In dimensionality reduction most methods aim at preserving one or a few properties of the original space in the resulting embedding. As our results show, preserving the sparse representation of the signals from the original space in the (lower) dimensional projected space is beneficial for several benchmarks (faces, traffic signs, and handwritten digits). The intuition behind is that taking a sparse representation for the different samples as point of departure highlights the important correlations among the samples that one then wants to exploit to arrive at the final, effective low-dimensional embedding. We explicitly adapt the LPP and LLE techniques to work with the sparse representation criterion and compare to the original methods on the referenced databases, and this for both unsupervised and supervised cases. The improved results corroborate the usefulness of the proposed sparse representation based linear and non-linear projections.

Cited by 58Open access

A Sparse Representation Perspective

Allan Y. Yang, John Wright, Yi Ma et al. · 2007

personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission. Acknowledgement We would like to thank Dr. Harry Shum, Dr. Xiaoou Tang and many others at Microsoft Research in Asia for helpful and informative discussions on face recognition, during our visit there in fall 2006. We also thank Prof. Harm Derksen and Prof. Michael Wakin of the University of Michigan, Prof. Robert Fossum and Yoav Sharon of the University of Illinois for advice and discussions on polytope geometry and sparse representation. This work was partially supported by the grants of ARO MURI W911NF-06-1-0076,

Cited by 0

Sparse Representation With Kernels

Shenghua Gao, Ivor W. Tsang, Liang-Tien Chia · IEEE Transactions on Image Processing · 2012

Recent research has shown the initial success of sparse coding (Sc) in solving many computer vision tasks. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which helps in finding a sparse representation of nonlinear features, we propose kernel sparse representation (KSR). Essentially, KSR is a sparse coding technique in a high dimensional feature space mapped by an implicit mapping function. We apply KSR to feature coding in image classification, face recognition, and kernel matrix approximation. More specifically, by incorporating KSR into spatial pyramid matching (SPM), we develop KSRSPM, which achieves a good performance for image classification. Moreover, KSR-based feature coding can be shown as a generalization of efficient match kernel and an extension of Sc-based SPM. We further show that our proposed KSR using a histogram intersection kernel (HIK) can be considered a soft assignment extension of HIK-based feature quantization in the feature coding process. Besides feature coding, comparing with sparse coding, KSR can learn more discriminative sparse codes and achieve higher accuracy for face recognition. Moreover, KSR can also be applied to kernel matrix approximation in large scale learning tasks, and it demonstrates its robustness to kernel matrix approximation, especially when a small fraction of the data is used. Extensive experimental results demonstrate promising results of KSR in image classification, face recognition, and kernel matrix approximation. All these applications prove the effectiveness of KSR in computer vision and machine learning tasks.

Cited by 193

Human detection using sparse representation

G. Krishna Vinay, Sk. Mohammadul Haque, R. Venkatesh Babu et al. · 2012

The problem of human detection is challenging, more so, when faced with adverse conditions such as occlusion and background clutter. This paper addresses the problem of human detection by representing an extracted feature of an image using a sparse linear combination of chosen dictionary atoms. The detection along with the scale finding, is done by using the coefficients obtained from sparse representation. This is of particular interest as we address the problem of scale using a scale-embedded dictionary where the conventional methods detect the object by running the detection window at all scales.

Cited by 5

Sparse Representation On Single Image

Jin Tan, Taiping Zhang, Yuan Yan Tang · 2019

In recent years, sparse representation of vector signals has been successfully applied in the field of pattern recognition. However, this approach can not be used for single image, as it may require the dictionary to be overcomplete. In addition, the sparse coefficients lack some geometric explanations. This work proposes a novel sparse coding technique on single image. This sparse coding coefficients have explicitly the geometric explanations of images. It depicts the structure information of the image which is robust to variations in illumination, expression, and occlusion. Therefore, the sparse coding coefficients can be used for feature representation of images on small sample case. Experiments on face databases demonstrate the effectiveness of the new sparse coding model.

Cited by 0