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Convolutional sparse and Low-Rank coding-Based Image Decomposition

IEEE TRANSACTIONS ON IMAGE PROCESSING 2018年第5期27卷 2121-2133页

作者： Zhang, He Patel, Vishal M. Rutgers Univ New Brunswick Dept Elect & Comp Engn New Brunswick NJ 08901 USA

We propose novel convolutional sparse and low-rank coding-based methods for cartoon and texture decomposition. In our method, we first learn a set of generic filters that can efficiently represent cartoon-and texture-type images. Then, using these learned filters, we propose two optimization frameworks to decompose a given image into cartoon and texture components: convolutional sparse coding-based image decomposition;and convolutional low-rank coding-based image decomposition. By working directly on the whole image, the proposed image separation algorithms do not need to divide the image into overlapping patches for leaning local dictionaries. The shift-invariance property is directly modeled into the objective function for learning filters. Extensive experiments show that the proposed methods perform favorably compared with state-of-theart image separation methods.

关键词： Image decomposition convolutional coding low-rank coding sparse coding

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Unsupervised domain adaptation for regression using dictionary learning

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KNOWLEDGE-BASED SYSTEMS 2023年 267卷

作者： Dhaini, Mohamad Berar, Maxime Honeine, Paul Van Exem, Antonin Univ Rouen Normandy LITIS Rouen France Tellux Rouen France

Unsupervised domain adaptation aims to generalize the knowledge learned on a labeled source domain across an unlabeled target domain. Most of existing unsupervised approaches are feature-based methods that seek to find domain invariant features. Despite their wide applications, these approaches proved to have some limitations especially in regression tasks. In this paper, we study the problem of unsupervised domain adaptation for regression tasks. We highlight the obstacles faced in regression compared to a classification task in terms of sensitivity to the scattering of data in feature space. We take this issue and propose a new unsupervised domain adaptation approach based on dictionary learning. We seek to learn a dictionary on source data and follow an optimal direction trajectory to minimize the residue of the reconstruction of the target data with the same dictionary. For stable training of a neural network, we provide a robust implementation of a projected gradient descent dictionary learning framework, which allows to have a backpropagation friendly end-to-end method. Experimental results show that the proposed method outperforms significantly most of state-of-the-art methods on several well-known benchmark datasets, especially when transferring knowledge from synthetic to real domains.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Domain adaptation Transfer learning Regression Deep learning Dictionary learning sparse coding

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The neural representation of an auditory spatial cue in the primate cortex

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CURRENT BIOLOGY 2024年第10期34卷 2162-2174.e5页

作者： Undurraga, Jaime A. Luke, Robert Van Yper, Lindsey Monaghan, Jessica J. M. McAlpine, David Macquarie Univ Dept Linguist 16 Univ Ave Sydney NSW 2109 Australia Tech Univ Denmark Interacoust Res Unit Bldg 352 DK-2800 Lyngby Denmark Bion Inst 384-388 Albert St East Melbourne Vic 3002 Australia Univ Southern Denmark Inst Clin Res DK-5230 Odense Denmark Odense Univ Hosp Res Unit ORL Head & Neck Surg & Audiol DK-5230 Odense Denmark Univ Southern Denmark DK-5230 Odense Denmark Australian Hearing Hub Natl Acoust Labs 16 Univ Ave Sydney NSW 2109 Australia Macquarie Univ Hearing 16 Univ Ave Sydney NSW 2109 Australia Macquarie Univ Australian Hearing Hub 16 Univ Ave Sydney NSW 2109 Australia

Humans make use of small differences in the timing of sounds at the two ears-interaural time differences (ITDs)-to locate their sources. Despite extensive investigation, however, the neural representation of ITDs in the human brain is contentious, particularly the range of ITDs explicitly represented by dedicated neural detectors. Here, using magneto- and electro-encephalography (MEG and EEG), we demonstrate evidence of a sparse neural representation of ITDs in the human cortex. The magnitude of cortical activity to sounds presented via insert earphones oscillated as a function of increasing ITD-within and beyond auditory cortical regions-and listeners rated the perceptual quality of these sounds according to the same oscillating pattern. This pattern was accurately described by a population of model neurons with preferred ITDs constrained to the narrow, sound -frequency -dependent range evident in other mammalian species. When scaled for head size, the distribution of ITD detectors in the human cortex is remarkably like that recorded in vivo from the cortex of rhesus monkeys, another large primate that uses ITDs for source localization. The data solve a long-standing issue concerning the neural representation of ITDs in humans and suggest a representation that scales for head size and sound frequency in an optimal manner.

关键词： binaural hearing binaural processing brain imaging behavioral neural code magnetoencephalography electroencephalography computational models sparse coding

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sparse coding of humpback whale songs for their survey

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TRAITEMENT DU SIGNAL 2013年第3-5期30卷 219-242页

作者： Doh, Yann Razik, Joseph Paris, Sebastien Adam, Olivier Glotin, Herve Univ Sud Toulon Var CNRS UMR 7296 Equipe DYNI Lab Sci Informat & Syst F-83957 La Garde France Inst Univ France IUF F-75005 Paris France Aix Marseille Univ CNRS UMR 7296 Equipe DYNI Lab Sci Informat & Syst F-13284 Marseille France CNRS Inst Jean Le Rond dAlembert UMR 7190 F-75015 Paris France Univ Paris 11 CNRS UMR 8195 Ctr Neurosci Paris Sud Equipe Bioacoust F-91405 Orsay France

Male humpback whales emit songs during the breeding season. These songs are made with successive vocalizations called sound units. The study of these songs is based on the classification of these sound units, especially to extract the song theme of the singers in a specific area during a specific season. Recently, some approaches are proposed for automatic classification of these sound units. This paper introduces the sparse coding as a robust unsupervised classifier to generate efficient time-frequency representation of the calls of the whale. Secondly, the subunit shows to be interesting to analyze the evolution of the humpback whale songs during two years. It is statistically shown that the shortest units are the most stable (occurring with similar time frequency shape across the two years), while the longest units are evolving from one year to one other.

关键词： sparse coding humpback whale sound units

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AFDL: a new adaptive fuzzy dictionary learning for medical image classification

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PATTERN ANALYSIS AND APPLICATIONS 2021年第1期24卷 145-164页

作者： Ghasemi, Majid Kelarestaghi, Manoochehr Eshghi, Farshad Sharifi, Arash Islamic Azad Univ Dept Comp Engn Sci & Res Branch Tehran Iran Kharazmi Univ Dept Elect & Comp Engn Fac Engn Tehran Iran

sparse coding allows the representation of complex data as a linear combination of basis sparse vectors (alternatively called atoms or codewords), a collection of which constitutes a dictionary. Dictionary learning is a learning process aimed at finding a small number of optimal basis vectors for a more accurate representation of the original data. The existing dictionary learning methods do not address the inherent uncertainty of the input data in their learning processes. To compensate for the uncertainty, and to obtain a flexible and effective learning system, we introduce a new adaptive fuzzy dictionary learning (AFDL) method for image classification purposes. The new method iteratively alternates between sparse coding based on a given dictionary and an adaptive fuzzy dictionary learning approach to learn (improve) dictionary atoms. The adjustability of the dictionary and coefficients vectors, in this method, provide us a more accurate and straight representation of input data. AFDL was applied on magnetic resonance images from the cancer image archive datasets, for medical image classification of cancer tumors. Finally, the overall experimental results clearly show that our approach outperforms its rival techniques in terms of accuracy, sensitivity, and specificity. Convergence speed in the experimental results shows that AFDL can achieve its acceptable precision in a reasonable time.

关键词： sparse coding Adaptive fuzzy dictionary learning Medical image classification sparse representation

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Electrocardiogram-Based Biometric Identification Using Mixed Feature Extraction and sparse Representation

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SENSORS 2023年第22期23卷 9179-9179页

作者： Zhang, Xu Liu, Qifeng He, Dong Suo, Hui Zhao, Chun Jilin Univ Coll Elect Sci & Engn State Key Lab Integrated Optoelect Changchun 130015 Peoples R China Jilin Univ Sch Preparatory Educ Changchun 130015 Peoples R China

(1) Background: The ability to recognize identities is an essential component of security. Electrocardiogram (ECG) signals have gained popularity for identity recognition because of their universal, unique, stable, and measurable characteristics. To ensure accurate identification of ECG signals, this paper proposes an approach which involves mixed feature sampling, sparse representation, and recognition. (2) Methods: This paper introduces a new method of identifying individuals through their ECG signals. This technique combines the extraction of fixed ECG features and specific frequency features to improve accuracy in ECG identity recognition. This approach uses the wavelet transform to extract frequency bands which contain personal information features from the ECG signals. These bands are reconstructed, and the single R-peak localization determines the ECG window. The signals are segmented and standardized based on the located windows. A sparse dictionary is created using the standardized ECG signals, and the KSVD (K-Orthogonal Matching Pursuit) algorithm is employed to project ECG target signals into a sparse vector-matrix representation. To extract the final representation of the target signals for identification, the sparse coefficient vectors in the signals are maximally pooled. For recognition, the co-dimensional bundle search method is used in this paper. (3) Results: This paper utilizes the publicly available European ST-T database for our study. Specifically, this paper selects ECG signals from 20, 50 and 70 subjects, each with 30 testing segments. The method proposed in this paper achieved recognition rates of 99.14%, 99.09%, and 99.05%, respectively. (4) Conclusion: The experiments indicate that the method proposed in this paper can accurately capture, represent and identify ECG signals.

关键词： electrocardiogram (ECG) biometric wavelet sparse coding dictionary learning

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sparse Spatial coding: A Novel Approach to Visual Recognition

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2014年第6期23卷 2719-2731页

作者： Oliveira, Gabriel Leivas Nascimento, Erickson R. Vieira, Antonio Wilson Montenegro Campos, Mario Fernando Univ Minnesota Dept Comp Sci Minneapolis MN 55455 USA Univ Fed Minas Gerais Dept Comp Sci BR-31270901 Belo Horizonte MG Brazil Univ Estadual Montes Claros Dept Math & Comp Sci BR-39440 Montes Claros Brazil

Successful image-based object recognition techniques have been constructed founded on powerful techniques such as sparse representation, in lieu of the popular vector quantization approach. However, one serious drawback of sparse space-based methods is that local features that are quite similar can be quantized into quite distinct visual words. We address this problem with a novel approach for object recognition, called sparse spatial coding, which efficiently combines a sparse coding dictionary learning and spatial constraint coding stage. We performed experimental evaluation using the Caltech 101, Caltech 256, Corel 5000, and Corel 10000 data sets, which were specifically designed for object recognition evaluation. Our results show that our approach achieves high accuracy comparable with the best single feature method previously published on those databases. Our method outperformed, for the same bases, several multiple feature methods, and provided equivalent, and in few cases, slightly less accurate results than other techniques specifically designed to that end. Finally, we report state-of-the-art results for scene recognition on COsy Localization Dataset (COLD) and high performance results on the MIT-67 indoor scene recognition, thus demonstrating the generalization of our approach for such tasks.

关键词： Object recognition image coding learning (artificial intelligence) computer vision vision and scene undertanding sparse coding

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A multi-class structured dictionary learning method using discriminant atom selection

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PATTERN ANALYSIS AND APPLICATIONS 2021年第2期24卷 685-700页

作者： Rolon, Roman E. Di Persia, Leandro E. Spies, Ruben D. Rufiner, Hugo L. Consejo Nacl Invest Cient & Tecn UNL Inst Invest Sen Sistemas & Inteligencia Computac Sinc IFICH Santa Fe Argentina Consejo Nacl Invest Cient & Tecn UNL FIQ Inst Matemat Aplicada LitoralIMAL Santa Fe Argentina

In the last decade, traditional dictionary learning methods have been successfully applied to various pattern classification tasks. Although these methods produce sparse representations of signals which are robust against distortions and missing data, such representations quite often turn out to be unsuitable if the final objective is signal classification. In order to overcome, or at least to attenuate, such a weakness, several new methods which incorporate discriminant information into sparse-inducing models have emerged in recent years. In particular, methods for discriminant dictionary learning have shown to be more accurate than the traditional ones, which are only focused on minimizing the total representation error. In this work, we present both a novel multi-class discriminant measure and an innovative dictionary learning method. For a given dictionary, this new measure, which takes into account not only when a particular atom is used for representing signals coming from a certain class and the magnitude of its corresponding representation coefficient, but also the effect that such an atom has in the total representation error, is capable of efficiently quantifying the degree of discriminability of each one of the atoms. On the other hand, the new dictionary construction method yields dictionaries which are highly suitable for multi-class classification tasks. Our method was tested with two widely used databases for handwritten digit recognition and for object recognition, and compared with three state-of-the-art classification methods. The results show that our method significantly outperforms the other three achieving good recognition rates and additionally, reducing the computational cost of the classifier.

关键词： Multi-class discriminant measure Structured dictionary learning sparse coding Handwritten digit recognition Object recognition

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Eye State Detection Based On Nonnegative sparse coding Feature

Eye State Detection Based On Nonnegative Sparse Coding Featu...

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2014 4th IEEE International Conference on Information Science and Technology

作者： Chen Siyao Qin Jianzhao Smart Cities(Hong Kong)Limited Smart Cities (Hong Kong) Limited

It is very useful to detect our eye open or close state in some sistuations. For example, it will give much help to the drivers for driving with drowsiness. In this paper, we propose an efficient method which is made up of four steps for eye state detection: Eye Localization, Preprocessing, Nonnegative sparse coding feature, SVM classifier. We compared our method to other methods[6][7] in our collected dataset which contains the eyes with glasses or in darkness, and so on. In our method, we will preprocess the image for eliminating the illumination and then analyze the eye image feature based on nonnegative sparse coding. It is proved to be very effective in our dataset.

关键词： Eye State Preprocessing sparse coding SVM

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Parametric Convolutional Dictionary Learning and its Applications to Seismic Data Processing

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： Chen, Hongling Sacchi, Mauricio D. Gao, Jinghuai Xi An Jiao Tong Univ Fac Elect & Informat Engn Sch Informat & Commun Engn Xian 710049 Peoples R China Univ Alberta Dept Phys Edmonton AB T6G 2E1 Canada

Convolutional dictionary learning (CDL) can represent signals and images via the superposition of components given by the convolution of sparse coefficients (features) and the elements of a dictionary (filters). The filters represent universal signals that can model different images, whereas the coefficients are intrinsic to one particular image. Estimating the coefficients and the filters from a set of observed signals is similar to a blind deconvolution problem where we aim to simultaneously represent a signal via the convolution of two unknown signals. Classical CDL provides data-dependent filters that, in the seismic data processing case, might not have a solid resemblance to typical waveforms that one observes in seismic records. This limits the dictionary's representation and discriminability, thus suffering from suboptimal denoising or reconstruction results. To address this challenge, we propose a new CDL algorithm. The proposed approach introduces a parametric constraint to enforce simplicity on the filters, guiding the learning process toward a more efficient and structured representation of the data. Specifically, we restrict each filter to include one single waveform parametrizable via a second-order traveltime curve and a seismic wavelet. The learned dictionary comprises linear and parabolic events that adapt adequately to observed seismic waveforms and resemble local Radon transform basis functions. The alternating direction method of multipliers (ADMMs) is adopted to solve the proposed parametric convolutional learning problem. The experimental results demonstrate that the proposed method achieves superior reconstruction results compared to the existing convolutional and patch-based dictionary learning methods.

关键词： Dictionaries Convolution Machine learning Data processing Transforms Image reconstruction Convolutional codes Convolutional dictionary learning (CDL) data reconstruction seismic data sparse coding

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