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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Qian, Qipeng Xiong, Fengchao Zhou, Jun Shanghai Jiao Tong Univ Sch Math Sci Shanghai Peoples R China Zhejiang Univ Coll Comp Sci Hangzhou Peoples R China Griffith Univ Sch Informat & Commun Technol Nathan Qld Australia

ISBN: (纸本)9781538691540

In this paper, we propose a novel approach for spectral unmixing by unfolding the iterative shrinkage-thresholding algorithm (ISTA) into a deep neural network architecture. Spectral unmixing aims at identifying the endmembers and their fractional abundances in the mixed pixels. Once the endmembers are obtained as a dictionary, abundance estimation can be defined as a sparse coding problem with nonnegativity constraint. There are a number of iterative optimization algorithms for solving this problem, including ISTA, however, they always require hundreds and even thousands iterations, which is too slow for time-sensitive applications. In contrast, deep neural networks can approximate a finite closed-form expression to direct estimate abundances by learning from training samples, but they are closer to black-box mechanism rather than problem-level formulations. Deep unfolding constructs a deep neural network architecture inspired by the problem model and its corresponding optimization algorithm, which incorporates the prior knowledge of physical model and algorithm into network architecture. In this paper, the deep unfolded ISTA model is adopted for abundance estimation. It uses only a small training set to learn the model parameters, and then the abundance estimation become to be a feed-forward process in this model, which is very fast since no iteration is required.

关键词： Deep unfolding spectral unmixing sparse coding iterative methods

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sparse coding for Flexible, Robust 3D Facial-Expression Synthesis

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IEEE COMPUTER GRAPHICS AND APPLICATIONS 2012年第2期32卷 76-88页

作者： Lin, Yuxu Song, Mingli Dao Thi Phuong Quynh He, Ying Chen, Chun Zhejiang Univ Coll Comp Sci Hangzhou 310003 Zhejiang Peoples R China Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore

Computer animation researchers have been extensively investigating 3D facial-expression synthesis for decades. However, flexible, robust production of realistic 3D facial expressions is still technically challenging. A proposed modeling framework applies sparse coding to synthesize 3D expressive faces, using specified coefficients or expression examples. It also robustly recovers facial expressions from noisy and incomplete data. This approach can synthesize higher-quality expressions in less time than the state-of-the-art techniques.

关键词： Animation Bayesian tensor analysis Computational modeling computer graphics expression retargeting expression synthesis Face recognition Facial animation facial-expression analogy graphics and multimedia incomplete data Noise measurement noisy data Robustness sparse coding Three dimensional displays

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ONLINE ADAPTIVE DICTIONARY LEARNING AND WEIGHTED sparse coding FOR ABNORMALITY DETECTION

ONLINE ADAPTIVE DICTIONARY LEARNING AND WEIGHTED SPARSE CODI...

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IEEE International Conference on Image Processing

作者： Sheng Han Ruiqing Fu Suzhen Wang Xinyu Wu Shenzhen Key Lab for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences The Chinese University of Hong Kong

ISBN: (纸本)9781479923427

This paper focuses mainly on adaptive dictionary updating and abnormality detection via weighted space coding in video surveillance. Generally, abnormality analysis conducted on a large amount of video data is very complicated, time-consuming and time-variant. However, our dictionary is very efficient at following up on shifted contents in video and abandoning old inactive information in time. The adaptability characteristic also helps reduce the dictionary's size to a small scale, since it only needs to keep recent or active information. We also introduce a simple, but effective, judgement criterion for abnormal detection based on sparse coding over weighted bases. Because of the condensed dictionary and the simplified judgment criterion, our algorithm performs online learning and online detection with a high speed and a high accuracy in various scenes.

关键词： Dictionary Learning Adaptive Learning sparse coding Abnormality Detection sparse coding anomaly detection video data Dictionaries as Topic video monitoring

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Time Warp Invariant Dictionary Learning for Time Series Clustering: Application to Music Data Stream Analysis

Time Warp Invariant Dictionary Learning for Time Series Clus...

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European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Yazdi, Saeed Varasteh Douzal-Chouakria, Ahlame Gallinari, Patrick Moussallam, Manuel Univ Grenoble Alpes LIG Grenoble INP CNRS Grenoble France Univ Paris 06 Paris France Deezer Paris France

ISBN: (纸本)9783030109257;9783030109240

This work proposes a time warp invariant sparse coding and dictionary learning framework for time series clustering, where both input samples and atoms define time series of different lengths that involve variable delays. For that, first an l(0) sparse coding problem is formalised and a time warp invariant orthogonal matching pursuit based on a new cosine maximisation time warp operator is proposed. A dictionary learning under time warp is then formalised and a gradient descent solution is developed. Lastly, a time series clustering based on the time warp sparse coding and dictionary learning is presented. The proposed approach is evaluated and compared to major alternative methods on several public datasets, with an application to deezer music data stream clustering. Data related to this paper are available at: The link to the data and the evaluating algorithms are provided in the paper. Code related to this paper is available at: The link will be provided at the first author personal website (http://***/similar to varasteh/).

关键词： Time series clustering Dictionary learning sparse coding

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Learning Scale and Shift-Invariant Dictionary for sparse Representation 5th

Learning Scale and Shift-Invariant Dictionary for Sparse Rep...

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5th International Conference on Machine Learning, Optimization, and Data Science (LOD)

作者： Aritake, Toshimitsu Murata, Noboru Waseda Univ Grad Sch Adv Sci & Engn Tokyo Japan

ISBN: (纸本)9783030375997;9783030375980

sparse representation is a signal model to represent signals with a linear combination of a small number of prototype signals called atoms, and a set of atoms is called a dictionary. The design of the dictionary is a fundamental problem for sparse representation. However, when there are scaled or translated features in the signals, unstructured dictionary models cannot extract such features. In this paper, we propose a structured dictionary model which is scale and shift-invariant to extract features which commonly appear in several scales and locations. To achieve both scale and shift invariance, we assume that atoms of a dictionary are generated from vectors called ancestral atoms by scaling and shift operations, and an algorithm to learn these ancestral atoms is proposed.

关键词： sparse coding Dictionary learning Scale-invariance Shift-invariance

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LEARNING SPATIALLY-CORRELATED TEMPORAL DICTIONARIES FOR CALCIUM IMAGING 44

LEARNING SPATIALLY-CORRELATED TEMPORAL DICTIONARIES FOR CALC...

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Mishne, Gal Charles, Adam S. Yale Univ Dept Math New Haven CT 06520 USA Princeton Univ Princeton Neurosci Inst Princeton NJ 08540 USA

ISBN: (纸本)9781479981311

Calcium imaging has become a fundamental neural imaging technique, aiming to recover the individual activity of hundreds of neurons in a cortical region. Current methods ( mostly matrix factorization) are aimed at detecting neurons in the field-of-view and then inferring the corresponding time-traces. In this paper, we reverse the modeling and instead aim to minimize the spatial inference, while focusing on finding the set of temporal traces present in the data. We reframe the problem in a dictionary learning setting, where the dictionary contains the time-traces and the sparse coefficient are spatial maps. We adapt dictionary learning to calcium imaging by introducing constraints on the norms and correlations of the time-traces, and incorporating a hierarchical spatial filtering model that correlates the time-trace usage over the field-of-view. We demonstrate on synthetic and real data that our solution has advantages regarding initialization, implicitly inferring number of neurons and simultaneously detecting different neuronal types.

关键词： Calcium imaging Dictionary learning sparse coding Two-photon microscopy Re-weighted l(1)

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sparse Representations on DW-MRI: A study on pancreas 19

Sparse Representations on DW-MRI: A study on pancreas

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19th Annual IEEE International Conference on Bioinformatics and Bioengineering (BIBE)

作者： Pentari, Anastasia Tsagkatakis, Grigorios Marias, Kostas Manikis, Georgios C. Kartalis, Nikolaos Papanikolaou, Nikolaos Tsakalides, Panagiotis Univ Crete Dept Comp Sci Crete NE USA Fdn Res & Technol Hellas FORTH Inst Comp Sci Iraklion Greece Hellen Mediterranean Univ Dept Elect & Comp Engn Iraklion Greece Karolinska Univ Hosp Dept Radiol Stockholm Sweden Champalimaud Fdn Ctr Unknown Clin Computat Imaging Grp Lisbon Portugal

ISBN: (纸本)9781728146171

This paper presents a method for reducing the Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) examination time based on the mathematical framework of sparse representations. The aim is to undersample the b-values used for DW-MRI image acquisition which reflect the strength and timing of the gradients used to generate the DW-MRI images since their number defines the examination time. To test our method we investigate whether the undersampled DW-MRI data preserve the same accuracy in terms of extracted imaging biomarkers. The main procedure is based on the use of the k-Singular Value Decomposition (k-SVD) and the Orthogonal Matching Pursuit (OMP) algorithms, which are appropriate for the sparse representations computation. The presented results confirm the hypothesis of our study as the imaging biomarkers extracted from the sparsely reconstructed data have statistically close values to those extracted from the original data. Moreover, our method achieves a low reconstruction error and an image quality close to the original.

关键词： sparse coding Dictionary Learning DW-MRI b-value IVIM

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Secure sparse Representations in L0 Norm Minimization and Its Application to EtC Systems 13

Secure Sparse Representations in L0 Norm Minimization and It...

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13th IEEE International Conference on Signal Processing and Communication Systems (ICSPCS)

作者： Nakachi, Takayuki Kiya, Hitoshi NTT Corp NTT Network Innovat Labs Yokosuka Kanagawa 2390847 Japan Tokyo Metropolitan Univ Sch Syst Design Hino Tokyo 1910065 Japan

ISBN: (纸本)9781728121949

In this paper, we propose a method to estimate secure sparse representations in L0 norm minimization and its application to Encryption-then-Compression (EtC) systems. The proposed scheme provides a practical Orthogonal Matching Pursuit (OMP) algorithm that allows computation in the encrypted domain. We prove, theoretically, that the proposal has exactly the same estimation performance as the unencrypted variant of the OMP algorithm. We demonstrate the security strength of the proposed secure sparse representations. Even if the dictionary information is leaked, the proposed scheme protects the privacy information of the observed signals.

关键词： sparse coding Secure Computation Orthogonal Matching Pursuit (OMP) Random Unitary Transform EtC Systems

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POLYNOMIAL NETWORKS REPRESENTATION OF NONLINEAR MIXTURES WITH APPLICATION IN UNDERDETERMINED BLIND SOURCE SEPARATION 44

POLYNOMIAL NETWORKS REPRESENTATION OF NONLINEAR MIXTURES WIT...

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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Wang, Lu Ohtsuki, Tomoaki Keio Univ Grad Sch Sci & Technol Fujisawa Kanagawa Japan Keio Univ Dept Informat & Comp Sci Fujisawa Kanagawa Japan

ISBN: (纸本)9781479981311

Similar to the deep architectures, a novel multi-layer architecture is used to extend the linear blind source separation (BSS) method to the nonlinear case in this paper. The approach approximates the nonlinearities based on a polynomial network, where the layer of our network begins with the polynomial of degree 1, up to build an output layer that can represent data with a small bias by a good approximate basis. Relying on several transformations of the input data, with higher-level representation from lower-level ones, the networks are to fulfill a mapping implicitly to the high-dimensional space. Once the polynomial networks are built, the coefficient matrix can be estimated by solving an l(1)-regularization on the coding coefficient vector. The experiment shows that the proposed approach exhibits a higher separation accuracy than the comparison algorithms.

关键词： Underdetermined BSS vanishing polynomial networks nonlinear mixture sparse coding time-frequency representation

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MULTI-STAGE OMP sparse coding Using Local Matching Pursuit Atoms Selection

MULTI-STAGE OMP Sparse Coding Using Local Matching Pursuit A...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Mahdi Marsousi Kaveh Abhari Paul Babyn Javad Alirezaie Department of Electrical and Computer Engineering University of Toronto Canada Department of Electrical and Computer Engineering Ryerson University Canada Department of Medical Imaging University of Saskatchewan and Saskatoon Health Region Canada

ISBN: (纸本)9781479903573

A new multi-stage approach based on component extraction is proposed to more efficiently address the sparse representation problem. In each stage a pre-set number of coefficients are chosen for reconstructing each signal component. A global search is performed to extract a lower dimensional sub-dictionary consisting of a sorted set of candidate atoms to represent the signal component, corresponding to the stage. The best representing atoms are then selected from the sub dictionary using the Matching Pursuit (MP) method. Afterwards, the sparse coefficients are updated in the same manner in which the Orthogonal Matching Pursuit (OMP) operates. The proposed method is more efficient that the conventional OMP methods. To evaluate the performance of the proposed method, it is compared to OMP and Stagewise OMP (StOMP), which are conceptually the most similar to the proposed approach. The results illustrate the proposed method is more time efficient than OMP and more robust and sparser than the StOMP.

关键词： OMP gene matching pursuits component extraction Atoms sparse coding

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