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Subspace Clustering by Integrating Sparseness and Spatial-Closeness Priors

Journal of Physics: Conference Series 2020年第1期1631卷

作者： Zhe Li Haodong Pei Liang He Jiaming Liu Jiaxin Hu Dongji Wang Futian Power Supply Bureau Shenzhen Power Supply Bureau Co. Ltd Shenzhen Guangdong 518001 China Key Laboratory of Intelligent Infrared Perception Chinese Academy of Sciences Shanghai 200083 China Shanghai Institute of Technical Physics of the Chinese Academy of Sciences Shanghai 200083 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China

How to construct an effective sample affinity matrix is an important problem for subspace clustering, and most existing subspace clustering algorithms pursue the affinity matrix in a single space. In this paper, we propose a novel computational framework for subspace clustering, called Complementary Subspace Clustering (CSC) at first, where the affinity matrix is constructed in a pair of complementary spaces which provide different and complementary constraints on the affinity matrix. Many existing structural priors on self representation and dimensionality reduction can be seamlessly integrated into the CSC framework. Then under this framework, we explore a simple and effective subspace clustering algorithm by respectively introducing two basic priors - sparse representation and spatial closeness - into the referred pair of spaces. Moreover, a kernel variant of the proposed clustering algorithm is present. Extensive experimental results demonstrate that although only basic priors are involved, the explored algorithms from the CSC framework can improve the clustering performances significantly when the number of the sample classes is relatively big.

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Segmentation-based Euler number with multi-levels for image feature description

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Procedia Computer Science 2017年 111卷 245-251页

作者： Qian Zhang Lin Wang Jiang-Hao Yu Minggui Zhang Academic Affairs Office Guizhou Minzu University GuiYang 550025 China Pattern Recognition & Intelligent System Key Laboratory of Gui Zhou Province GuiYang 550025 China

This paper proposes a new and efficient image feature descriptor using Euler Number with the help of segmentation according to given number of levelsest. The proposed Segmentation-based Euler Number for image description algorithm (SENA) works as the following steps. First, transforming the image into gray image if the image is color image; then, dividing the gray image into different sets using the given number of levelsets; next, decomposing the gray image into binary images with multi-thresh using the otsu algorithm; following, computing the Euler Number of each binary image; finally, combining the Euler Numbers, mean and variance to form the feature vector for an input image. The proposed SENA was employed to the image classification on three public available dataset (Stanford Dogs Dataset, 17 flower dataset, and Caltech 256 dataset). We compute SENA with LBP and Gabor on the Stanford Dogs Dataset, the detail classification results on 17 flower dataset is given as confusion matrix, and the result of SENA on the Caltech 256 dataset is compared with those of the recently reported. The experiments demonstrate a competitive performance of SENA for classification task.

关键词： Number image feature image segmentation image classification LevelSet

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Progressive Refinement Bilateral Filter

Progressive Refinement Bilateral Filter

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IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Chanyi Lu Yong Zhao Lin Wang Guiying Zhang Fujian Feng Li Zhang College of Data Science and Information Engineering Guizhou Minzu University Guiyang China School of Electronic and Computer Engineering Shenzhen Graduate School of Peking University Shenzhen China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China Department of Medical Information Engineering Zunyi Medical University Zunyi China

ISBN: (纸本)9781538678626;9781538678619

In this paper, a coarse-to-fine framework for image noise removal is proposed. The bilateral filter is redefined by the manner of progressive refining to effectively eliminate noise, thus forming progressive refinement bilateral filter (PRBF), which estimates the pixel values of a noisy image through the method of progressive refinement with gradual recursion and its result retains more details, infinite close to the original image. PRBF is further integrated into the denoising model for the details processing again after denoising of the major part of the image. Our algorithm is evaluated and compared on two large standardized datasets and a number of selected images. The experimental results show that our algorithm consistently outperforms other approaches for image denoising and demonstrates the effectiveness of our framework for stereo matching.

关键词： Noise reduction Image denoising Mathematical model Filtering Filtering algorithms Kernel Biomedical imaging

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THE JOINT EFFECT OF SEMANTIC AND SYNTACTIC WORD EMBEDDINGS ON SENTIMENT ANALYSIS 5

THE JOINT EFFECT OF SEMANTIC AND SYNTACTIC WORD EMBEDDINGS O...

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2016 5th IEEE International Conference on Network Infrastructure and Digital Content(IC-NIDC 2016)

作者： Shu Chen Guang Chen Wei Wang Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications

ISBN: (纸本)9781509012473

Employing pre-trained word embeddings as preliminary features in convolutional neural networks（CNN） for natural language processing（NLP） tasks has been proved to be of *** exploit this idea by taking advantage of different types of word embeddings at the same *** be specific,we extend CNN models to coordinate two lookup tables,which exploit semantic word embeddings and syntactic word embeddings at the same *** test our models on several review datasets and all results indicate the positive effect on sentiment *** understand the reason behind,we explore the difference of the two word embeddings and how they influence the CNN models.

关键词： Word embedding Semantic and Syntactic similarity Convolutional neural networks Sentiment analysis

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CNN-based invertible wavelet scattering for the investigation of diffusion properties of the in vivo human heart in diffusion tensor imaging

arXiv

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arXiv 2019年

作者： Deng, Zeyu Wang, Lihui Kuai, Zixiang Chen, Qijian Cheng, Xinyu Yang, Feng Yang, Jie Zhu, Yuemin Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province College of Computer Science and Technology Guizhou University Guiyang550025 China Imaging Center Harbin Medical University Cancer Hospital Harbin150081 China School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China University Lyon INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 LyonF-69621 France

In vivo diffusion tensor imaging (DTI) is a promising technique to investigate noninvasively the fiber structures of the in vivo human heart. However, signal loss due to motions remains a persistent problem in in vivo cardiac DTI. We propose a novel motion-compensation method for investigating in vivo myocardium structures in DTI with free-breathing acquisitions. The method is based on an invertible Wavelet Scattering achieved by means of Convolutional Neural Network (WSCNN). It consists of first extracting translation-invariant wavelet scattering features from DW images acquired at different trigger delays and then mapping the fused scattering features into motion-compensated spatial DW images by performing an inverse wavelet scattering transform achieved using CNN. The results on both simulated and acquired in vivo cardiac DW images showed that the proposed WSCNN method effectively compensates for motion-induced signal loss and produces in vivo cardiac DW images with better quality and more coherent fiber structures with respect to existing methods, which makes it an interesting method for measuring correctly the diffusion properties of the in vivo human heart in DTI under free breathing. Copyright © 2019, The Authors. All rights reserved.

关键词： Diffusion tensor imaging

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Learning data-adaptive nonparametric kernels

arXiv

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arXiv 2018年

作者： Liu, Fanghui Huang, Xiaolin Gong, Chen Yang, Jie Li, Li Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Department of Automation Tsinghua University

Kernel methods have been extensively used in a variety of machine learning tasks such as classification, clustering, and dimensionality reduction. For complicated practical tasks, the traditional kernels, e.g., Gaussian kernel and sigmoid kernel, or their combinations are often not sufficiently flexible to fit the data. In this paper, we present a Data-Adaptive Nonparametric Kernel (DANK) learning framework in a data-driven manner. To be specific, in model formulation, we impose an adaptive matrix on the kernel/Gram matrix in an entry-wise strategy. Since we do not specify the formulation of the adaptive matrix, each entry in the adaptive matrix can be directly and flexibly learned from the data. Therefore, the solution space of the learned kernel is largely expanded, which makes our DANK model flexible to capture the data with different local statistical properties. Specifically, the proposed kernel learning framework can be seamlessly embedded to support vector machines (SVM) and support vector regression (SVR), which has the capability of enlarging the margin between classes and reducing the model generalization error. Theoretically, we demonstrate that the objective function of our DANK model embedded in SVM/SVR is gradient-Lipschitz continuous. Thereby, the training process for kernel and parameter learning in SVM/SVR can be efficiently optimized in a unified framework. Further, to address the scalability issue in nonparametric kernel learning framework, we decompose the entire optimization problem in DANK into several smaller easy-to-solve problems, so that our DANK model can be efficiently approximated by this partition. The effectiveness of this approximation is demonstrated by both empirical studies and theoretical guarantees. Experimentally, the proposed DANK model embedded in SVM/SVR achieves encouraging performance on various classification and regression benchmark datasets when compared with other representative kernel learning based algorithms. Copyrig

关键词： Matrix algebra

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J-Measure Based Pruning for Advancing Classification Performance of Information Entropy Based Rule Generation

J-Measure Based Pruning for Advancing Classification Perform...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Han Liu Mihaela Cocea Weili Ding School of Computer Science and Informatics Cardiff University Queen’s Buildings 5 The Parade Cardiff United Kingdom School of computing University of Portsmouth Buckingham Building Lion Terrace Portsmouth United Kingdom Laboratory of Pattern Recognition and Intelligent Systems Key Laboratory of Industrial Computer Control Engineering of Heibei Provience Yanshan University Qinghuangdao China

ISBN: (纸本)9781538652152

Learning of classification rules is a popular approach of machine learning, which can be achieved through two strategies, namely divide-and-conquer and separate-and-conquer. The former is aimed at generating rules in the form of a decision tree, whereas the latter generates if-then rules directly from training data. From this point of view, the above two strategies are referred to as decision tree learning and rule learning, respectively. Both learning strategies can lead to production of complex rule based classifiers that overfit training data, which has motivated researchers to develop pruning algorithms towards reduction of overfitting. In this paper, we propose a J-measure based pruning algorithm, which is referred to as Jmean-pruning. The proposed pruning algorithm is used to advance the performance of the information entropy based rule generation method that follows the separate and conquer strategy. An experimental study is reported to show how Jmean-pruning can effectively help the above rule learning method avoid overfitting. The results show that the use of Jmean-pruning achieves to advance the performance of the rule learning method and the improved performance is very comparable or even considerably better than the one of C4.5.

关键词： Decision trees Learning systems Training data Entropy Information entropy Machine learning Machine learning algorithms

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Dynamic round robin scheduling algorithm for μc/OS-III

Boletin Tecnico/Technical Bulletin

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Boletin Tecnico/Technical Bulletin 2017年第7期55卷 8-15页

作者： Zhang, Chunhong Ren, Jianqiang Luo, Ping College of Mathematics and Information Langfang Teachers University Langfang Hebei065000 China Institute of Pattern Recognition and Intelligent System Langfang Teachers University Langfang Hebei065000 China Langfang Key Laboratory of Intelligent Transportation System Langfang Hebei065000 China

In μC/OS-III, the traditional round robin algorithm is inefficient for tasks with the same priority. To solve this problem, this paper proposes a dynamic round robin scheduling algorithm based on the shortest remaining time first, called SRTFDRR. The algorithm determines the scheduling order according to the submission time and the remaining execution time of each task;For tasks with the same priority, set up multiple ready queues, each queue calculates the time slice independently based on the average value of the remaining execution time of the task;The ready task is inserted into the ready queue RQ1 of the corresponding priority in the order of burst time increments;If the priority of the queue task is the current highest priority, the SRTFDRR algorithm runs the task in the queue RQ1;If the time slice expires, there is a task in RQ1 that has not yet finished, and the remaining execution part moves into queue RQ2 waiting for scheduling again, and so on until all tasks are finished. A multi-level queue is formed during the scheduling process. Simulation results show that, compared with the traditional RR algorithm, the SRTFDRR algorithm reduces the average turnaround time, average waiting time, average response time and switching times. It performs well in performance, overhead, and fairness of scheduling.

关键词： Scheduling

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Deep crisp boundaries: From boundaries to higher-level tasks

arXiv

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arXiv 2018年

作者： Wang, Yupei Zhao, Xin Li, Yin Huang, Kaiqi Center for Research on Intelligent System and Engineering Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Department of Biostatistics and Medical Informatics Department of Computer Sciences Univeristy of Wisconsin-Madison Center for Research on Intelligent System and Engineering National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology 100190

Edge detection has made significant progress with the help of deep Convolutional Networks (ConvNet). These ConvNet based edge detectors have approached human level performance on standard benchmarks. We provide a systematical study of these detectors' outputs. We show that the detection results did not accurately localize edge pixels, which can be adversarial for tasks that require crisp edge inputs. As a remedy, we propose a novel refinement architecture to address the challenging problem of learning a crisp edge detector using ConvNet. Our method leverages a top-down backward refinement pathway, and progressively increases the resolution of feature maps to generate crisp edges. Our results achieve superior performance, surpassing human accuracy when using standard criteria on BSDS500, and largely outperforming state-of-the-art methods when using more strict criteria. More importantly, we demonstrate the benefit of crisp edge maps for several important applications in computer vision, including optical flow estimation, object proposal generation and semantic segmentation. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

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SOUND CLASSIFICATION BASED ON SPECTROGRAM FOR SURVEILLANCE APPLICATIONS 5

SOUND CLASSIFICATION BASED ON SPECTROGRAM FOR SURVEILLANCE A...

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2016 5th IEEE International Conference on Network Infrastructure and Digital Content(IC-NIDC 2016)

作者： Yingjie Li Gang Liu Pattern Recognition and Intelligent System Laboratory School of Information and Communication EngineeringBeijing University of Posts and Telecommunications

ISBN: (纸本)9781509012473

This paper presents an audio event classification algorithm which automatically classifies an audio event as footstep,glass breaking,gunshot or scream mainly for surveillance ***,the Gabor feature of the audio spectrogram is extracted,there are two kinds of Gabor features,namely global Gabor feature and local Gabor *** we use Principal Components Analysis(PCA) and Linear Discriminant Analysis(LDA) to compress the feature dimension,finally the K nearest neighbor classifier(KNN) is used to recognize audio *** carried out extensive experiments on the clean and noisy audio *** results demonstrate that the algorithm is able to guarantee a recall of 96.1%on clean sets and is proved to be more effective than traditional methods.

关键词： Spectrogram Global Gabor feature Local Gabor feature PCA LDA

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