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Chinese Automation Congress (CAC)

作者： Zihao Wang Enmei Tu Meng Zhou Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The process of labeling samples costs time and resources but unlabeled samples are easier to obtain. Recently, graph-based deep semi-supervised learning (GDSSL) training a deep network using a small number of labeled samples and the abundant unlabeled samples has been demonstrated to be promising on image classification tasks. These methods construct a graph to represent the structure of the input data (or hidden features). The successes of these GDSSL algorithms depend upon the structure of the similarity graph. However, existing GDSSL approaches construct the graph using predefined rules (such as knn graph) or fixed similarity measures (such as Gaussian kernel), which may limit the potential of GDSSL. In this paper, we move further in this direction to propose a novel end-to-end GDSSL approach which fully optimizes the whole graph without such limitations. To this end, we concatenate two neural networks (feature network and similarity network) together to learn the categorical label and semantic similarity, respectively, and train the networks with a new regularization term, the extended graph Laplacian, to minimize a unified objective function. Extensive experiments on several benchmark datasets demonstrate that our approach could outperform existing approaches on image classification. Furthermore, as a side-product, the similarity network could give faithful semantic similarity measure of samples, which is not possessed by other GDSSL approaches.

关键词： Semantics Training Laplace equations Semisupervised learning Task analysis Neural networks Linear programming

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Towards unbiased random features with lower variance for stationary indefinite kernels

arXiv

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arXiv 2021年

作者： Luo, Qin Fang, Kun Yang, Jie Huang, Xiaolin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

Random Fourier Features (RFF) demonstrate well-appreciated performance in kernel approximation for large-scale situations but restrict kernels to be stationary and positive definite. And for non-stationary kernels, the corresponding RFF could be converted to that for stationary indefinite kernels when the inputs are restricted to the unit sphere. Numerous methods provide accessible ways to approximate stationary but indefinite kernels. However, they are either biased or possess large variance. In this article, we propose the generalized orthogonal random features, an unbiased estimation with lower variance. Experimental results on various datasets and kernels verify that our algorithm achieves lower variance and approximation error compared with the existing kernel approximation methods. With better approximation to the originally selected kernels, improved classification accuracy and regression ability is obtained with our approximation algorithm in the framework of support vector machine and regression. © 2021, CC BY.

关键词： Approximation algorithms

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Weighted Total Variation constrained reconstruction for reduction of metal artifact in CT

Weighted Total Variation constrained reconstruction for redu...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Yanbo Zhang Xuanqin Mou Hao Yan Institute of Image Processing & Pattern Recognition Xi''an Jiaotong University Xi'an Shaanxi China

TV constrained reconstruction could obtain perfect results from incomplete data, and has been applied to reduce metal artifact by assuming that the projection contaminated by metal is missing. In TV constrained reconstruction, the selection of a proper step parameter for TV minimization procedure is a key point. However, this parameter is usually selected empirically, and it is a constant for all pixels in the whole image domain, regardless of the difference of missing projection quantity at different pixels. By analyzing the relationship between the missing projections and pixels position, a Weighted Total Variation (WTV) constrained reconstruction method is proposed to reduce metal artifact in this paper. For WTV constrained method, the parameters are no longer the same, but vary over image domain as the introduced information miss rate. The simulation results show that the proposed method is more effective than current TV constraint to reduce metal artifact. Moreover, WTV constrained method is extended to other incomplete projection problems.

关键词： TV image reconstruction Metals Pixel Computed tomography X-ray imaging Noise

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Low energy-consuming cluster-based algorithm to enforce integrity and preserve privacy in data aggregation

Low energy-consuming cluster-based algorithm to enforce inte...

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13th International Symposium on Distributed Computing and Applications to Business,Engineering & Science（DCABES 2014）

作者： Zhengwei Guo Xiaojiao Ding College of Computer & Information Engineering Institute of Image Processing & Pattern Recognition Henan University

This paper proposes a low energy-consuming cluster-based algorithm to protect data integrity and privacy named ILCCPDA, which can dynamically elect cluster head by LEACH clustering protocol and take the simple cluster fusion approach to reduce the data transmission, thus reducing energy consumption. ILCCPDA can detect data integrity by adding homomorphic message authentication code and take the random key distribution mechanism for data encryption. It can solve the problem of the integrity, privacy and energy consumption in the wireless transmission of sensor data.

关键词： wireless sensor networks(WSN) data aggregation privacy protection

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Multi-regularization parameters estimation for Gaussian mixture classifier based on MDL principle

Multi-regularization parameters estimation for Gaussian mixt...

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International Conference on Neural Computation Theory and Applications, NCTA 2011

作者： Zhou, Xiuling Guo, Ping Philip Chen, C.L. Laboratory of Image Processing and Pattern Recognition Beijing Normal University Beijing 100875 China Artificial Intelligence Institute Beijing City University Beijing China Faculty of Science and Technology University of Macau Macau China

ISBN: (纸本)9789898425843

Regularization is a solution to solve the problem of unstable estimation of covariance matrix with a small sample set in Gaussian classifier. And multi-regularization parameters estimation is more difficult than single parameter estimation. In this paper, KLIM-L covariance matrix estimation is derived theoretically based on MDL (minimum description length) principle for the small sample problem with high dimension. KLIM-L is a generalization of KLIM (Kullback-Leibler information measure) which considers the local difference in each dimension. Under the framework of MDL principle, multi-regularization parameters are selected by the criterion of minimization the KL divergence and estimated simply and directly by point estimation which is approximated by two-order Taylor expansion. It costs less computation time to estimate the multi-regularization parameters in KLIM-L than in RDA (regularized discriminant analysis) and in LOOC (leave-one-out covariance matrix estimate) where cross validation technique is adopted. And higher classification accuracy is achieved by the proposed KLIM-L estimator in experiment.

关键词： Parameter estimation

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Radio frequency interference mitigation using pseudoinverse learning autoencoders

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Research in Astronomy and Astrophysics 2020年第8期20卷 121-128页

作者： Hong-Feng Wang Mao Yuan Qian Yin Ping Guo Wei-Wei Zhu Di Li Si-Bo Feng Image Processing and Pattern Recognition Laboratory School of Artificial IntelligenceBeijing Normal UniversityBeijing 100875China CAS Key Laboratory of FAST National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China School of Information Management Dezhou UniversityDezhou 253023China Image Processing and Pattern Recognition Laboratory School of System ScienceBeijing Normal UniversityBeijing 100875China Institute for Astronomical Science Dezhou UniversityDezhou 253023China University of Chinese Academy of Sciences Beijing 100049China Hanvon Technology Co. LtdBeijing 100193China NAOC-UKZN Computational Astrophysics Centre University of KwaZulu-NatalDurban 4000South Africa

Radio frequency interference(RFI)is an important challenge in radio *** comes from various sources and increasingly impacts astronomical observation as telescopes become more *** this study,we propose a fast and effective method for removing RFI in pulsar *** use pseudo-inverse learning to train a single hidden layer auto-encoder(AE).We demonstrate that the AE can quickly learn the RFI signatures and then remove them from fast-sampled spectra,leaving real pulsar *** method has the advantage over traditional threshold-based filter method in that it does not completely remove contaminated channels,which could also contain useful astronomical information.

关键词： pulsars:general methods:numerical methods:data analysis

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Improved MBNN for image segmentation

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Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications 2005年第SUPPL. 2期28卷 55-57+61页

作者： Cai, Nian Yang, Jie Hu, Kuang-Hu Xiong, Hai-Tao Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200240 China Institute of Biophysics Chinese Academy of Sciences Beijing 100101 China

An improved MBNN (model-based neural network) was proposed to segment images. An image model obtained by the Markov random filed (MRF) was introduced into the MBNN. The MRF's parameters were estimated by modified expectation-maximization (EM) algorithm. The technique of pre-assigning a class number was employed to decrease the computation burden. Therefore the task of image segmentation was implemented by the network. The experiment results show that it is feasible to apply the improved MBNN to image segmentation since a priori knowledge is excellently combined with local statistical correlation.

关键词： image segmentation

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A MIL-based interactive approach for hotspot segmentation from bone scintigraphy

A MIL-based interactive approach for hotspot segmentation fr...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Shijie Geng Jingyang Ma Xiaoguang Niu Shaoyong Jia Yu Qiao Jie Yang Institute of Image Processing and Pattern Recognition The Key Laboratory of System Control and Information Processing Ministry of Education China

ISBN: (纸本)9781479999897

Bone scintigraphy is widely used to diagnose bone diseases. Accurate hotspot segmentation is a critical task for tumor metastasis diagnosis. In this paper, we propose an interactive approach to detect and extract hotspots in thoracic region based on a new multiple instance learning (MIL) method called EM-MILBoost. We convert the segmentation problem to a multiple instance learning task by constructing positive and negative bags according to the input bounding box. In order to be robust against noisy input, we train a region-level hotspot classifier with EM-MILBoost and develop several segmentation strategies based on it. The experimental results demonstrate that our method outperforms other methods and is robust against various noisy input.

关键词： image segmentation Noise measurement Bones Training Indexes Belts Standards

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Extraction of complex wavelet features for iris recognition

Extraction of complex wavelet features for iris recognition

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作者： He, Xiaofu Shi, Pengfei Department of Computer Science and Technology East China Normal University Shanghai 200241 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9781424421756

This paper presents a new feature extraction method for iris recognition. Since two dimensional complex wavelet transform (2D-CWT) does not only keep wavelet transform's properties of multiresolution decomposition analysis and perfect reconstruction, but also adds its new merits: approximate shift invariance, good directional selectivity for 2-D image, and limited redundancy, which are useful for iris feature extraction. So, a set of high frequency 2D-CWT coefficients are selected as features for iris recognition. The phase information of the coefficients is used for feature encoding and Hamming distance is adopted for classification. Experimental results show that the proposed algorithm can get good recognition rate. © 2008 IEEE.

关键词： Hamming distance

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A Closed Form Solution to Segment 3D Motion Using Straight-line Optical Flow

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Journal of Shanghai Jiaotong university(Science) 2008年第1期13卷 76-80页

作者：张婧石繁槐马文娟刘允才 Institute Image Processing and Pattern Recognition Shanghai Jiaotong University School of Materials Science and Engineering Shanghai Jiaotong University

A closed form solution to the problem of segmenting multiple 3D motion models was proposed from straight-line optical flow. It introduced the multibody line optical flow constraint (MLOFC), a polynomial equation relating motion models and line parameters. The motion models can be obtained analytically as the derivative of the MLOFC at the corresponding line measurement, without knowing the motion model associated with that line. Experiments on real and synthetic sequences were also presented.

关键词： segmentation optical flow line generalized principal component analysis （GPCA）

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