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检索条件"主题词=sparse coding"

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EXPLOITING CORRELATION IN NEURAL SIGNALS FOR DATA COMPRESSION

EXPLOITING CORRELATION IN NEURAL SIGNALS FOR DATA COMPRESSIO...

引用

European Signal Processing Conference

作者： S. Schmale J. Hoeffmann B. Knoop G. Kreiselmeyer H. Hamer D. Peters-Drolshagen S. Paul Institute of Electrodynamics and Microelectronics (ITEM.me) University of Bremen Epilepsy Center of Erlangen (EZE) University Hospital of Erlangen

ISBN: (纸本)9781479946037

Progress in invasive brain research relies on signal acquisition at high temporal- and spatial resolutions, resulting in a data deluge at the (wireless) interface to the external world. Hence, data compression at the implant site is necessary in order to comply with the neurophysiological restrictions, especially when it comes to recording and transmission of neural raw data. This work investigates spatial correlations of neural signals, leading to a significant increase in data compression with a suitable sparse signal representation before the wireless data transmission at the implant site. Subsequently, we used the correlation-aware two-dimensional DCT used in image processing, to exploit spatial correlation of the data set. In order to guarantee a certain sparsity in the signal representation, two paradigms of zero forcing are evaluated and applied: Significant coefficients- and block sparsity-zero forcing.

关键词： Neural Signals Correlation Data Compression Compressed Sensing sparse coding

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Deep sparse Representations for Land-Use Scene Classification in Remote Sensing Images

Deep Sparse Representations for Land-Use Scene Classificatio...

引用

2016 IEEE 13th International Conference on Signal Processing（ICSP2016）

作者： Fan Hu Gui-Song Xia Liangpei Zhang Key State Laboratory LIESMARS Wuhan University Electronic Information School Wuhan University

Land-use scene classification which provides highlevel interpretation for remote sensing images is a challenging task, and the performance of this task mainly lies in the representative ability of extracted scene-level features. Motivated by the good generalization ability of deep convolutional neural network(CNN), we design a novel method, called deep sparse representation(DSR), building scene-level features via directly transferring CNN models pre-trained on general-purpose image dataset for land-use scene classification. We first extract deep CNN features in a feedforward way, and then compute sparse codes for the deep features through optimizing a sparse coding formulation;we finally max-pool the obtained sparse codes to form the image representations. Extensive experiments on a public land-use scene benchmark demonstrate that the image features generated by the proposed DSR, even with a simple linear classifier, can result in remarkable classification accuracies,which now become the new state-of-the-art performance on the benchmark.

关键词： scene classification CNN sparse coding feature representation

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Image classification: A hierarchical dictionary learning approach

Image classification: A hierarchical dictionary learning app...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Shahin Mahdizadehaghdam Liyi Dai Hamid Krim Erik Skau Han Wang North Carolina State University Department of Electrical and Computer Engineering Raleigh United States of America Army Research Office RTP Raliegh NC United States of America

ISBN: (纸本)9781509041183

Hierarchical dictionary learning seeks multiple dictionaries at different image scales to capture complementary coherent characteristics. We propose a method to learn a hierarchy of two overcomplete synthesis dictionaries with an image classification goal. The classification objective in some sense regularizes the joint optimization of the hierarchical dictionaries and injects refinement feedback. The validation of the proposed approach is based on its classification performance using two well-known data sets.

关键词： image classification dictionary learning sparse coding dimension reduction image processing Image classification sparse coding Dictionaries as Topic Image processing Hierarchical

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EXPLOITING MULTI-SCALE SPATIAL STRUCTURES FOR SPARSITY BASED SINGLE IMAGE SUPER-RESOLUTION

EXPLOITING MULTI-SCALE SPATIAL STRUCTURES FOR SPARSITY BASED...

引用

IEEE International Conference on Image Processing

作者： Yongqin Zhang Jiaying Liu Wei Bai Zongming Guo Inst. of Comput. Sci. & Technol. Peking Univ. Beijing China

ISBN: (纸本)9781479957521

To improve the performance of sparsity-based single image super-resolution (SR), we propose a joint SR framework of structure prior based sparse representation (SPSR). The proposed SPSR algorithm exploits the multi-scale spatial structural self-similarities, the gradient prior and nonlocally centralized sparse representation to formulate a constrained optimization problem for high-resolution image recovery. The high-resolution image is firstly initialized by exploiting cross-scale patch redundancy in an image pyramid from single input low-resolution image. Then the sparse modeling of the image SR problem is proposed to refine it further, where the gradient histogram preservation is incorporated as a regularization term. Finally, an iterative solution is provided to solve the problem of model parameter estimation and sparse representation. Experimental results on image super-resolution validate the generality, effectiveness and robustness of the proposed SPSR algorithm.

关键词： gradient methods image representation image resolution optimisation SPSR algorithm constrained optimization problem cross-scale patch redundancy gradient histogram preservation gradient prior high-resolution image recovery image pyramid iterative solution joint SR framework multiscale spatial structural self-similarities nonlocally centralized sparse representation sparsity based single image super-resolution structure prior based sparse representation Dictionaries Histograms Image reconstruction Principal component analysis Signal resolution Spatial resolution Image super-resolution dictionary learning self-similarities sparse coding sparse representation image pyramid sparse representation super-resolution signal resolution image resolution iterative solution Dictionaries as Topic histograms sparse coding gradient methods image representation image reconstruction self-similarity principal components analysis Spatial resolution

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A COCHLEAR NEURON BASED ROBUST FEATURE FOR SPEAKER RECOGNITION

A COCHLEAR NEURON BASED ROBUST FEATURE FOR SPEAKER RECOGNITI...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Datao You Tao Jiang Jiqing Han Tieran Zheng School of Computer Science and Technology Harbin Institute of Technology Harbin China

ISBN: (纸本)9781457705380

In this paper, a robust feature for text-independent speaker recognition is proposed, which simulate the response mode of cochlear neurons in processing acoustic signal. The feature is derived from sparse coding coefficient which is computed on a learned over-complete dictionary, and the dictionary is considered similar to part of speech sensitive cochlear neurons. Furthermore, the feature is generated without dimension reducing and de-correlation. The robust feature is implemented to address the problem of mismatch situation between training and testing. Experiments show that the proposed feature outperforms the Mel-frequency cepstral coefficients (MFCC) feature, especially under noisy environments, the equal error rate (EER) of the MFCC drops to 21.6percent (10 dB) from 10.3percent (25 dB), while the EER of the proposed feature is also 6.6percent (10 dB) with no degradation.

关键词： Robust feature extraction cochlear neurons speaker recognition sparse coding auditory

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K-WEB: NONNEGATIVE DICTIONARY LEARNING FOR sparse IMAGE REPRESENTATIONS

K-WEB: NONNEGATIVE DICTIONARY LEARNING FOR SPARSE IMAGE REPR...

引用

IEEE International Conference on Image Processing

作者： Marco Bevilacqua Aline Roumy Christine Guillemot Marie-Line Alberi Morel INRIA Rennes Alcatel-Lucent Bell Labs France

ISBN: (纸本)9781479923427

This paper presents a new nonnegative dictionary learning method, to decompose an input data matrix into a dictionary of nonnegative atoms, and a representation matrix with a strict l~0-sparsity constraint. This constraint makes each input vector representable by a limited combination of atoms. The proposed method consists of two steps which are alternatively iterated: a sparse coding and a dictionary update stage. As for the dictionary update, an original method is proposed, which we call K-WEB, as it involves the computation of k WEighted Barycenters. The so designed algorithm is shown to outperform other methods in the literature that address the same learning problem, in different applications, and both with synthetic and "real" data, i.e. coming from natural images.

关键词： Dictionary learning sparse representations K-SVD NMF NMF protocol Dictionaries as Topic sparse representation images sparse coding Nonnegative representations incoming data Learning

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Detecting sensor level spoof attacks using joint encoding of temporal and spatial features

Detecting sensor level spoof attacks using joint encoding of...

引用

7th International Conference on Imaging for Crime Detection and Prevention (ICDP 2016)

作者： Jun Liu Ajay Kumar Hong Kong Polytechnic University Kowloon HK

ISBN: (纸本)9781510845244

Automated detection of sensor level spoof attacks is critical for success of surveillance technologies and the detection of crimes. This paper presents a new approach to more accurately detect such spoof face presentation attempts during the surveillance. The image data from the spoof samples not only illustrates the subtle texture difference in the spatial domain but is also accompanied by temporal differences as compared to those from the live/real human samples. Therefore, we investigate a new approach to encode the sparsity of these two different categories while combining these two cues from the acquired image sequences. The spatial and temporal information in the acquired image data and the sparse dictionary approach are used to encode such information in effectively separating the spoof and the live categories. This approach does not require large number of training samples, such as for the deep learning based methods, while achieving very good performance. The experimental results presented in this paper on publicly available database achieve outperforming results and illustrate the effectiveness of the proposed approach.

关键词： Face Mask Detection Spoof Face Attacks sparse coding Biometrics Antispoofing Techniques sparse coding Biometrics image data coding time information spatial domain Face Presentation Sensors

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EFFICIENT PARAMETER OPTIMIZATION FOR EXAMPLE-BASED DESIGN OF NONSEPARABLE OVERSAMPLED LAPPED TRANSFORM

EFFICIENT PARAMETER OPTIMIZATION FOR EXAMPLE-BASED DESIGN OF...

引用

IEEE International Conference on Image Processing

作者： Shogo MURAMATSU Masaki ISHII Zhiyu CHEN Niigata University Dept. of Electrical and Electronic Eng. Niigata University Graduate School of Science & Technology

ISBN: (纸本)9781467399623

This paper proposes an efficient design method of nonseparable oversampled lapped transform (NSOLT). NSOLT is a multidimensional redundant transform which satisfies the nonseparable, symmetric, real-valued, overlapped, compact-supported and perfectreconstruction property. A typical example-based design approach, which consists of sparse coding and parameter optimization, is applicable to NSOLT. In the previous implementation, however, the parameter optimization stage dominated the computation. The main reason is that the quasi-Newton method with numerical gradient of the objective function was adopted. To reduce the computational cost, the analytical gradient is derived and introduced. For further acceleration, the quasi-Newton method is replaced by stochastic gradient descent. Through some experiments, the significance of the proposed method is verified.

关键词： Structured dictionary learning Tight frame Stochastic gradient descent Filter banks sparse approximation Filter banks Parameter optimization design approach Newton method sparse coding Objective function Transform

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Learning Multisensory Neural Controllers for Robot Arm Tracking

Learning Multisensory Neural Controllers for Robot Arm Track...

引用

International Joint Conference on Neural Networks

作者： Lakshitha P. Wijesinghe Marco Antonelli Jochen Triesch Bertram E. Shi Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Frankfurt Institute of Advanced Studies Division of Biomedical Engineering The Hong Kong University of Science and Technology

ISBN: (纸本)9781509061839

Humans learn multisensory eye-hand coordination starting from infancy without supervision. For an example, they learn to track their hands by exploiting various sensory modalities, such as vision and proprioception. This integration occurs as they learn to perceive the world around them and their relationship to it. Most prior work has focused on the role of vision, as it is a primary sensory source for humans. However, it is interesting to study how vision and proprioception interact. We propose a system which combines visual and proprioceptive information to learn the eye-hand coordination skills that enable a robot to fixate its camera gaze on the end effector of its arm. In our model, visual cues are part of the feedback control loop, whereas proprioceptive cues are part of a feedforward control loop. Both controllers, as well as the sensory transform from raw visual information to a neural sensory representation are learned as the robot performs motor babbling movements. Visual information is encoded by sparse coding. The basis functions that emerge are similar to the receptive fields in the human visual cortex. An actor-critic reinforcement learning algorithm is used to drive eye motor neurons fusing visual and proprioceptive cues. We model and test the system in the iCub simulation environment. Our results suggest that these sensory modalities are capable of jointly learning model parameters to perform the tracking task. The evolved policy has characteristics that are qualitatively similar to the human oculomotor plant.

关键词： Developmental Robotics sparse coding Artificial Neural Networks Reinforcement Learning Hand-eye Coordination sparse coding Artificial neural networks Learning feedback control loop Sight end effectors learning (artificial intelligence) Robot Visualization Controllers Proprioception Arm spiral arm Sensation Receptive field

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DENOISING OF FULL RESOLUTION DIFFERENTIAL SAR INTERFEROGRAM BASED ON K-SVD TECHNIQUE

DENOISING OF FULL RESOLUTION DIFFERENTIAL SAR INTERFEROGRAM ...

引用

IEEE International Geoscience and Remote Sensing Symposium

作者： Chandrakanta Ojha Adele Fusco Michele Manunta IREA-CNR

ISBN: (纸本)9781479979301

The aim of this paper is to demonstrate the applicability of sparse representation to filter out the noise from SAR interferograms. More specifically, we consider an innovative approach for interferometric phase denoising, based on K-SVD technique applied to SAR interferograms, for estimating the noise-free underlying structure of the phase information. To examine the effectiveness of this approach, the method has been successfully tested both on simulated and real interferograms. In the latter case, the proposed approach has been evaluated using ENVISAT SAR data acquired over the area of Napoli (Italy). The good results achieved applying the proposed method demonstrate its effectiveness for denoising SAR interferograms.

关键词： sparse coding Orthogonal Matching Pursuit Full resolution SAR interferogram K-SVD L_0-minimization

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