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16th IEEE International Conference on Industrial Informatics (INDIN)

作者： Harada, Keiya Ohno, Yuta Nakamura, Yuichi Nishi, Hiroaki Keio Univ Grad Sch Sci & Technol Kohoku Ku 3-14-1 Hiyoshi Yokohama Kanagawa 2238522 Japan

ISBN: (纸本)9781538648292

In recent years, there have been rapid increases in the number of network-connected devices such as computers, smartphones, and Internet of Things devices. Thus, large amounts of data have been accumulated such as locational data, website search histories, and power usage data. These data are used in various types of services. However, these data cannot he used easily for secondary purposes in some countries because of privacy problems. Therefore, privacy protection is necessary to apply these data in secondary uses where data anonymization is the usual solution. Many conventional methods are used for anonymizing power usage data, but the conventional method has three problems. First, it cannot anonymize time-series data. Second, the information loss is so large in the conventional method that the anonymized data are no longer suitable for secondary uses. Third, the conventional method cannot preserve the type of electrical appliance used. In this study, we propose a method for anonymizing power demand data, where sparse coding is used to solve the three problems that affect the conventional method. The proposed method can anonymize time series-data and it allows data to be analyzed at a chosen time. The proposed method was used to anonymize power usage data from the Urban Design Center Misono (UDCMi) and the experimental error rate decreased compared with the conventional method. The dictionary produced using the proposed method represents the electrical appliance data.

关键词： anonymization dictionary error rate information loss k-anonymity Mondrian-clustering sparse coding

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Light Field Image sparse coding via CNN-Based EPI Super-Resolution 33

Light Field Image Sparse Coding via CNN-Based EPI Super-Reso...

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33rd IEEE International Conference on Visual Communications and Image Processing (IEEE VCIP)

作者： Zhao, Jinbo An, Ping Huang, Xinpeng Shan, Liang Ma, Ran Shanghai Univ Sch Commun & Informat Engn Shanghai Inst Adv Commun & Data Sci Shanghai 200444 Peoples R China

ISBN: (纸本)9781538644584

This paper proposes a novel light field (LF) image compression scheme by super resolving the epipolar plane image (EPI) via convolutional neural network (CNN). In the scheme, we first decompose the LF image into sub-aperture images (SAIs), and only one quarter of them are compressed on the encoding side to reduce the bitrate. On the decoding side, we use these selected SAIs to reconstruct the entire LF by taking advantage of the special structure of EPI. The low-resolution EPIs generated from the sparse SAIs are super resolved by using deep residual network and the output high-resolution EPIs are used to rebuild the dense SAIs. Experimental results show the superior performance of our scheme, which achieve 1.46 dB quality improvement and 35.85 percent bit rate reduction on average compared with the typical pseudo-sequence-based coding method.

关键词： light field compression sparse coding EPI super-resolution deep learning convolutional neural network

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Automatic Extraction of Abnormalities on Temporal CT Subtraction Images Using sparse coding and 3D-CNN 18

Automatic Extraction of Abnormalities on Temporal CT Subtrac...

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18th International Conference on Control, Automation and Systems (ICCAS)

作者： Koizumi, Yuichiro Miyake, Noriaki Lu, Huimin Kim, Hyoungseop Murakami, Seiichi Aoki, Takatoshi Kido, Shoji Kyushu Inst Technol 1-1 Sensui Kitakyushu Fukuoka 8048550 Japan Univ Occupat & Environm Hlth 1-1 Iseigaoka Kitakyushu 8078555 Japan Yamaguchi Univ 2-16-1 Tokiwadai Ube Yamaguchi 7538511 Japan

ISBN: (纸本)9788993215151

In recent years, the proportion of deaths from cancer tends to increase in Japan, especially the number of deaths from lung cancer is increasing. CT device is effective for early detection of lung cancer. However, there is concern that an increase in burden on doctors will be caused by high performance of CT improving. Therefore, by presenting the "second opinion" by the CAD system, it reduces the burden on the doctor. In this paper, we develop a CAD system for automatic detection of lesion candidate regions such as lung nodules or ground glass opacity (GGO) from 3D CT images. Our proposed method consists of three steps. In the first step, lesion candidate regions are extracted using temporal subtraction technique. In the second step, the image is reconstructed by sparse coding for the extracted region. In the final step, 3D Convolutional Neural Network (3D-CNN) identification using reconstructed images is performed. We applied our method to 51 cases and True Positive rate (TP) of 79.81 % and False Positive rate (FP) of 37.65 % are obtained.

关键词： Ground Glass Opacity Temporal Subtraction Technique sparse coding 3D Convolutional Neural Network

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Bayesian sparse Topical coding

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2019年第6期31卷 1080-1093页

作者： Peng, Min Xie, Qianqian Wang, Hua Zhang, Yanchun Tian, Gang Wuhan Univ Sch Comp Sci Wuhan 430072 Hubei Peoples R China Victoria Univ Ctr Appl Informat Melbourne Vic 8001 Australia

sparse topic models (STMs) are widely used for learning a semantically rich latent sparse representation of short texts in large scale, mainly by imposing sparse priors or appropriate regularizers on topic models. However, it is difficult for these STMs to model the sparse structure and pattern of the corpora accurately, since their sparse priors always fail to achieve real sparseness, and their regularizers bypass the prior information of the relevance between sparse coefficients. In this paper, we propose a novel Bayesian hierarchical topic models called Bayesian sparse Topical coding with Poisson Distribution (BSTC-P) on the basis of sparse Topical coding with sparse Groups (STCSG). Different from traditional STMs, it focuses on imposing hierarchical sparse prior to leverage the prior information of relevance between sparse coefficients. Furthermore, we propose a sparsity-enhanced BSTC, Bayesian sparse Topical coding with Normal Distribution (BSTC-N), via mathematic approximation. We adopt superior hierarchical sparse inducing prior, with the purpose of achieving the sparsest optimal solution. Experimental results on datasets of Newsgroups and Twitter show that both BSTC-P and BSTC-N have better performance on finding clear latent semantic representations. Therefore, they yield better performance than existing works on document classification tasks.

关键词： Document representation Bayesian topic model sparse coding hierarchical prior

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Unsupervised Temporal Feature Learning Based on sparse coding Embedded BoAW for Acoustic Event Recognition 19

Unsupervised Temporal Feature Learning Based on Sparse Codin...

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19th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2018)

作者： Zhang Liwen Han Jiqing Deng Shiwen Harbin Inst Technol Harbin Heilongjiang Peoples R China Harbin Normal Univ Harbin Heilongjiang Peoples R China

ISBN: (纸本)9781510872219

The performance of an Acoustic Event Recognition (AER) system highly depends on the statistical information and the temporal dynamics in the audio signals. Although the traditional Bag of Audio Words (BoAW) and the Gaussian Mixture Models (GMM) approaches can obtain more statistics information by aggregating multiple frame-level descriptors of an audio segment compared with the frame-level feature learning methods, its temporal information is unreserved. Recently, more and more Deep Neural Networks (DNN) based AER methods have been proposed to effectively capture the temporal information in audio signals, and achieved better performance, however, these methods usually required the manually annotated labels and fixed-length input during feature learning process. In this paper, we proposed a novel unsupervised temporal feature learning method, which can effectively capture the temporal dynamics for an entire audio signal with arbitrary duration by building direct connections between the BoAW histograms sequence and its time indexes using a non-linear Support Vector Regression (SVR) model. Furthermore, to make the feature representation have a better signal reconstruction ability, we embedded the sparse coding approach in the conventional BoAW framework. Compared with the BoAW and Convolutional Neural Network (CNN) baselines, experimental results showed our method brings improvements of 9.7% and 4.1% respectively.

关键词： acoustic event recognition temporal feature learning bag of audio words sparse coding

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LEARNED CONVOLUTIONAL sparse coding

LEARNED CONVOLUTIONAL SPARSE CODING

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

作者： Sreter, Hillel Giryes, Raja Tel Aviv Univ Sch Elect Engn Tel Aviv Israel

ISBN: (纸本)9781538646588

We propose a convolutional recurrent sparse auto-encoder model. The model consists of a sparse encoder, which is a convolutional extension of the learned ISTA (LISTA) method, and a linear convolutional decoder. Our strategy offers a simple strategy for learning a task-driven sparse convolutional dictionary (CD), and producing an approximate convolutional sparse code (CSC) over the learned dictionary. We trained the model to minimize reconstruction loss via gradient decent with back-propagation and have achieved competitve results to KSVD image denoising and to leading CSC methods in image inpainting requiring only a small fraction of their runtime.

关键词： sparse coding ISTA LISTA Convolutional sparse coding Neural networks

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Image Compression: sparse coding vs. Bottleneck Autoencoders

Image Compression: Sparse Coding vs. Bottleneck Autoencoders

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IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI)

作者： Watkins, Yijing Iaroshenko, Oleksandr Sayeh, Mohammad Kenyon, Garrett Los Alamos Natl Lab Los Alamos NM 87545 USA New Mexico Consortium Los Alamos NM USA Southern Illinois Univ Carbondale Carbondale IL 62901 USA

ISBN: (纸本)9781538665688

Bottleneck autoencoders have been actively researched as a solution to image compression tasks. However, we observed that bottleneck autoencoders produce subjectively low quality reconstructed images. In this work, we explore the ability of sparse coding to improve reconstructed image quality for the same degree of compression. We observe that sparse image compression produces visually superior reconstructed images and yields higher values of pixel-wise measures of reconstruction quality (PSNR and SSIM) compared to bottleneck autoencoders. In addition, we find that using alternative metrics that correlate better with human perception, such as feature perceptual loss and the classification accuracy, sparse image compression scores up to 18.06% and 2.7% higher, respectively, compared to bottleneck autoencoders. Although computationally much more intensive, we find that sparse coding is otherwise superior to bottleneck autoencoders for the same degree of compression.

关键词： Image Compression sparse coding Bottleneck Autoencoders Feature Perceptual Loss Thumbnails

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A Denoising Algorithm for CT Image Using Low-rank sparse coding

A Denoising Algorithm for CT Image Using Low-rank Sparse Cod...

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Conference on Medical Imaging - Image Processing

作者： Lei, Yang Xu, Dong Zhou, Zhengyang Wang, Tonghe Dong, Xue Liu, Tian Dhabaan, Anees Curran, Walter J. Yang, Xiaofeng Emory Univ Dept Radiat Oncol Atlanta GA 30322 USA Emory Univ Winship Canc Inst Atlanta GA 30322 USA Zhejiang Canc Hosp Dept Ultrasound Imaging Hangzhou 310022 Zhejiang Peoples R China Nanjing Univ Dept Radiol Nanjing Drum Tower Hosp Affiliated HospMed Sch Nanjing 210008 Jiangsu Peoples R China

ISBN: (数字)9781510616387

ISBN: (纸本)9781510616387

We propose a denoising method of CT image based on low-rank sparse coding. The proposed method constructs an adaptive dictionary of image patches and estimates the sparse coding regularization parameters using the Bayesian interpretation. A low-rank approximation approach is used to simultaneously construct the dictionary and achieve sparse representation through clustering similar image patches. A variable-splitting scheme and a quadratic optimization are used to reconstruct CT image based on achieved sparse coefficients. We tested this denoising technology using phantom, brain and abdominal CT images. The experimental results showed that the proposed method delivers state-of-art denoising performance, both in terms of objective criteria and visual quality.

关键词： CT denoising sparse coding

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Single Image Super-Resolution via the Implementation of the Hardware-Friendly sparse coding 37

Single Image Super-Resolution via the Implementation of the ...

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37th Chinese Control Conference (CCC)

作者： Dong, Zhekang Lai, Chun Sing Xu, Zhao Qi, Donglian Zhejiang Univ Coll Elect Engn Hangzhou 310027 Zhejiang Peoples R China Hong Kong Polytech Univ Hung Hom Dept Elect Engn Hong Kong Peoples R China Univ Oxford Dept Engn Sci Oxford OX1 3LZ England

ISBN: (纸本)9789881563958

sparse representation is one of the effective approaches to perform the feature extraction on the high-dimensional signals and has been widely used in the field of image processing. In this paper, a novel single image super-resolution (SR) reconstruction algorithm based on sparse coding is presented. Specifically. due to the fact that sparse coding has the potential to greatly facilitate the biological neural system and to deal with a large amount of complex data, while consuming a finite amount of energy. a meinristor crossbar array synthesized by a threshold-type memristor model is constructed for the implementation of a hardware-friendly sparse coding method, i.e., the Locally Competitive Algorithm (LCA). Meanwhile, the relevant online dictionary learning method is provided, along with the description of weight programming strategy. Furthermore, the specific description of the image SR via the LCA sparse coding is provided. For the verification purpose, a series of experiments are carried out to illustrate the validity and effectiveness of the entire scheme.

关键词： Image super-resolution sparse coding memristor crossbar array dictionary learning

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Multiple Manifold Regularized sparse coding for Multi-View Image Clustering 18

Multiple Manifold Regularized Sparse Coding for Multi-View I...

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27th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Zhu, Xiaofei Khoi Duy Vo Guo, Jiafeng Long, Jiangwu Chongqing Univ Technol Coll Comp Sci & Engn Chongqing Peoples R China Leibniz Univ Hannover Res Ctr L3S Hannover Germany Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9781450360142

Multi-view clustering has received an increasing attention in many applications, where different views of objects can provide complementary information to each other. Existing approaches on multiview clustering mainly focus on extending Non-negative Matrix Factorization (NMF) by enforcing the constraint over the coefficient matrices from different views in order to preserve their consensus. In this paper, we argue that it is more reasonable to utilize the high-level manifold consensus rather than the low-level coefficient matrix consensus to better capture the underlying clustering structure of the data. Moreover, it is also effective to utilize the sparse coding framework, instead of the NMF framework, to deal with the sparsity issue. To this end, we propose a novel approach, named Multiple Manifold Regularized sparse coding (MMRSC). Experimental results on two publicly available real-world image datasets demonstrate that our proposed approach can significantly outperform the state-of-the-art approaches for the multi-view image clustering task.

关键词： multi-view clustering sparse coding manifold consensus

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