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IEEE International Conference on Systems, Man and Cybernetics

作者： Xiaoyong Bian Chunfang Chen Yang Chen Xiongjun Fei Jingshan Tang Intelligent Information Processing and Real-time Industrial Systems Hubei Province Key Laboratory School of Computer Science and Technology Wuhan University of Science and Technology and Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan China School of Information Science and Engineering and Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and Technology Wuhan China School of Technology Michigan Technological University Michigan USA

Efficient and accurate classification of high-resolution scene remains a challenge of within-class diversity and between-class similarity due to rich image variations in viewpoint, object pose, spatial resolution and background. To address these issues, we propose a multi-branch convolutional neural network (MB-CNN), which focuses on tackling the problem of learning the appropriate representation of a high-resolution scene that is rich enough to discriminate between different semantic classes. First, pyramid scene parsing network (PSPNet) with minor modification is introduced to gather global object information. Then, an attention net is proposed to highlight transformation invariance and key regions for attention feature extraction. Finally, above two branches are fused with original input branch to learn consistently semantic class information and generate powerful predictions. Our approach achieves better performance favorably against state-of-the-arts on two publicly available scene datasets.

关键词： Conferences Cybernetics

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A comparison of four global land cover maps on a provincial scale based on China’s 30 m GlobeLand30 4th

A comparison of four global land cover maps on a provincial ...

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4th International Conference on Geo-Informatics in Resource Management and Sustainable Ecosystem, GRMSE 2016

作者： Ye, Xiaohui Zhao, Jinling Huang, Linsheng Zhang, Dongyan Hong, Qi Anhui Engineering Laboratory of Agro-Ecological Big Data Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China

ISBN: (纸本)9789811039652

To monitor the entire earth system, several global land cover mapping products have been produced based on different remote sensing imagery (e.g., AVHRR, MODIS, SPOT, HJ-1), including UMD Land Cover, Global Land Cover 2000, GlobCover 2009 and GlobeLand30. However, the application potential of those products has not been fully explored at a provincial scale. The primary objective of this study is to compare and investigate the potential of these products in Anhui Province, China. The area and spatial consistency were used to evaluate the four datasets based on China’s GlobeLand 30 due to 30 m spatial resolution and the total accuracy of 83.51%. The Pearson’s correlation coefficient (R) and the percentage disagreement (PD) were used to evaluate the area consistency. The spatial similarity coefficient (O) was here adopted in order to verify the accuracy of spatial positions. A total of eight cover classes including cropland, woodland, grassland, shrubland, wetland, water bodies, artificial surfaces and bareland were reclassified and mapped to perform the intercomparison. The analysis results show that the PD of GlobCover 2009 is respectively 15.36% and −8.43% for "cropland" and "woodland", while they are −20.20% and 2.10% for UMD. The "woodland" has better agreement percentage in comparison with "cropland". The O of UMD is the worst of 69.25%, indicating that the spatial consistency is weak for "cropland". Conversely, the O of GlobCover 2009 reaches up to 83.50%. © Springer Nature Singapore Pte Ltd. 2017.

关键词： Remote sensing

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Deep Multi-Metric Learning for Person Re-Identification

Deep Multi-Metric Learning for Person Re-Identification

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yongxin Ge Xinqian Gu Min Chen Hongxing Wang Dan Yang Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education (Chongqing University) Chongqing China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China School of Big Data & Software Engineering Chongqing University Chongqing China

In this paper, to exploit more discriminative information of the global-body and body-parts features, we present a novel deep multi-metric learning (DMML) network for person re-identification under the triplet framework. The main novelty of our learning framework lies in two aspects: 1) Unlike most existing metric learning-based approaches, which learn only one distance metric for comparison, our DMM-L method aims to learn different metrics for the global-body and body-parts features respectively by using convolutional neural network (CNN); 2) A new multi-metric loss function is proposed to train the DMML network, under which the distance of each negative pair is greater than that of each positive pair by a predefined margin, and the correlations of different metrics are maximized. Compared with the previous person re-identification methods that have shown state-of-the-art performances, our DMML approach can achieve competitive results on the challenging CUHK03, CUHKOl, VIPeR and iLIDS datasets.

关键词： Measurement Image color analysis Feature extraction Training Machine learning Kernel Learning systems

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Stability Analysis and Hopf Bifurcation Research for DNA Strand Displacement with Time Delay

Stability Analysis and Hopf Bifurcation Research for DNA Str...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Yanhong Liu Hui Lv Changjun Zhou Zhixiang Yin Xianwen Fang Zhonglong Zheng Qiang Zhang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China School of Mathematics and Big Data Anhui University of Science and Technology Huaian China College of Mathematics Zhejiang Normal University Jinhua China Department of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (纸本)9781538693810;9781538693803

In this paper, a nonlinear system with time delay mode for DNA strand displacement based on toehold exchange firstly is proposed. The boundedness of all the state variables of the DNA strand displacement system is proved. After that, local stability analysis is performed around the positive equilibrium point to obtain a necessary and sufficient condition for local stability, which depend on the coefficients of the characteristic equation corresponding to linearization of the system. In addition, the existence of the Hopf bifurcation is analyzed, which shows that the time delay parameter will guarantee that the system is stable within a certain range. However, Hopf bifurcation and chaos occur when the time delay parameter increases continuously and even exceeds the critical value. At last, numerical simulations have verified the correctness and validity of the DNA strand displacement studies.

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Multi-temporal MOD09A1-based detecting of major growth stages of paddy rice on a provincial scale 37

Multi-temporal MOD09A1-based detecting of major growth stage...

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37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017

作者： Huang, Linsheng Zhang, Shasha Zhao, Jinling Huang, Wenjiang Huang, Jinyang Qi, Xiaobo Anhui Engineering Laboratory Agro-Ecological Big Data Anhui University Hefei230601 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences Beijing100094 China

ISBN: (纸本)9781509049516

Growth stage information is a very primary parameter for managing the grain crop. In this study, time series Enhanced Vegetation Index (EVI) data in 2015 were obtained in Anhui Province, China based on 8-day composite MOD09A1 data products, and the extraction method of rice phenology was specifically analyzed at a provincial scale. HANTS (Harmonic Analysis of Time Series) filtering algorithm was firstly used to smooth the time series EVI curves and identify every growth and development period of paddy rice. The results indicate that HANTS has a better performance in removing noise, restoring original information and retain the change information of multi-temporal EVI curves. The primary planting regions were located in the southern Huai-River areas, from early June to late June in 2015. It turned into the transplanting stage in succession from south to north and followed by the heading stage in succession in early August. From early September to late October, it turned into mature stage from south to north in succession. © 2017 IEEE.

关键词： Indexes

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VideoGIS data retrieval based on multi-feature fusion

VideoGIS data retrieval based on multi-feature fusion

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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Haihong Dai Bin Hu Qian Cui Zhiqiang Zou College of Computer Nanjing University of Posts and Telecommunications Nanjing Jiangsu China College of Geographic Science Nanjing Normal University Nanjing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing Jiangsu China Key Laboratory of Virtual Geographic Environment (Nanjing Normal University) Ministry of Education Nanjing China

With the rapid development of smart cities and the advance of city safety and defense demands, the question that how to accurately discover and retrieve the data that users request from VideoGIS data faces a series of bottleneck problems. VideoGIS data retrieval is one of the important ways to solve above problems. In order to accelerate the rate of feature matching and improve the efficiency of the video retrieval, a new method of VideoGIS data retrieval based on multi-feature fusion is proposed in this paper. The method firstly use video frame difference based on Euclidean distance to extract the key frames under the spatial and temporal sampling of the video. On the basis of this, the global features (e.g. color, shape, texture) are fused by different weighted coefficients, then the feature vector, as the video multi-feature fusion representation, can be constructed by fusing the global features and local features. Based on the multi-feature fusion, correlation between video features is made full use. Compared with the method of single feature and two-feature fusion, the experimental results indicate that the proposed retrieval method has better retrieval effect.

关键词： Feature extraction Image color analysis data mining Correlation Euclidean distance Shape Streaming media

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Vehicle appearance feature recognition based on image

Vehicle appearance feature recognition based on image

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2016 6th International Conference on Machinery,Materials,Environment,Biotechnology and Computer(MMEBC 2016)

作者： Deyang Shao Chao Xu Shixian Luo Bo Feng Long Jiao Anhui Engineering Laboratory of Agro-Ecological Big Data Anhui University Key Laboratory of Intelligent Computer & Signal Processing Ministry of EducationAnhui University

ISBN: (纸本)9781510828087

Now, the highway toll system still uses a single license plate recognition, this method has a problem of inaccurate identificationFor this kind of situation, this paper put forward to increase the appearance of the vehicle feature information and can improve the accuracy of recognitionIn this paper, we adopt the ORB algorithm to extract the exterior feature information of the vehicle and two-way matching、RANSAC algorithms to remove mismatching pointsAt the same time, we continue to iteration the scale parameter of the affine transformation and rotation angle at the matching point as a kind of judgment, which improves the robustness of the algorithm.

关键词： Feature information ORB RANSAC Affine transformation

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Comparative analysis of identification methods for wheat leaf diseases

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International Agricultural Engineering Journal 2016年第3期25卷 197-203页

作者： Zhang, D.Y. Xie, X.H. Liang, D. Huang, L.S. Zhang, X.Q. Teng, L. Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China Anhui Engineering Laboratory of Agro-Ecological Big Data Anhui University School of Electronic and Information Engineering Hefei Anhui230601 China

How to fast, accurately and robustly recognize wheat diseases, particularly for those diseases with mild-to-moderate severity, is a challenge for prevention and control of crop disease timely. In this study, image processing technique was applied to segment the infected regions of disease leaves. Twenty disease features were extracted, and eighteen larger weight features were selected by Relief-F algorithm to generate the models of Support Vector Machine (SVM), Relevance Vector Machine (RVM) and Back Propagation Neural Network (BPNN). Subsequently, these models were used to identify two kinds of wheat diseases, namely, wheat stripe rust and powdery mildew. Total 136 samples, including 68 training samples and 68 test samples with different infection severities were used to study the recognition capabilities of the three models. Results showed that high predictive accuracies in identification of two wheat diseases with varying severity for all three models. Overall accuracy of RVM was 89.71%, which was superior to 83.82% of SVM and inferior to 92.64% of BPNN. Meanwhile, the recognition accuracies of SVM, RVM and BPNN models for mild-to-moderate disease were 83.33%, 88.33% and 91.67%, respectively. The prediction time of RVM was less than those of SVM and BPNN, with differences as large as 7.96 and 31.68 times, respectively. Therefore, RVM appeared to be the most suitable for real-time identifying wheat leaf diseases among the three models, which can provide important technical support for wheat diseases management.

关键词： Support vector machines

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Research on bearing micro deformation based on the frequency domain technique of white light speckle image

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Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument 2016年 37卷 46-52页

作者： Feng, Bo Xu, Chao Shao, Deyang Luo, Shixian Anhui Engineering Laboratory of Agro-Ecological Big Data Anhui University Hefei230601 China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei230601 China

In this paper, a new method of non contact and on-line measurement is presented for the change of the axis of the rotation axis. Under the condition of a constant light source, the image of the original position and the offset image in the original position are collected by the micro camera, These two pictures are calculated by the improved White light digital image point by point analysis method in this paper, and get the black and white stripes. The number and the angle of the fringe in the white light digital frequency domain interference image can be known as the offset and direction of the rotating shaft in the rotating process. Because this method only use a camera and a light source, and this method is very simple, and it can always detect, and it does not affect the normal work, so there is a wide range of industrial application prospects. This method can provide alarm before the plastic deformation occurs in the shaft, Reduce the risk of shaft damage. © 2016, Science Press. All right reserved.

关键词： Frequency domain analysis

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Enhancing Sequential Personalized Product Search with External Out-of-sequence Knowledge

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ACM Transactions on Information Systems 1000年

作者： Jiongnan Liu Zhicheng Dou Jian-Yun Nie Zhenlin Chen Guoyu Tang Sulong Xu Ji-Rong Wen Gaoling School of Artificial Intelligence Renmin University of China China DIRO University de Montreal Canada *** Inc China Gaoling School of Artificial Intelligence Renmin University of China China Engineering Research Center of Next-Generation Intelligent Search and Recommendation Ministry of Education China and Beijing Key Laboratory of Big Data Management and Analysis Methods China

A key challenge in personalized product search is to capture user’s preferences. Recent work attempted to model sequences of user historical behaviors, i.e., product purchase histories, to build user profiles and to personalize results accordingly. Although these approaches have demonstrated promising retrieval performances, we notice that most of them focus solely on the intra-sequence interactions between items. However, as there is usually a small amount of historical behavior data, the user profiles learned by these approaches could be very sensitive to the noise included in it. To tackle this problem, we propose incorporating out-of-sequence external information to enhance user modeling. More specifically, we inject the external item-item relations (e.g., belonging to the same brand), and query-query relations (e.g., the semantic similarities between them), into the intra-sequence interaction to learn better user profiles. In addition, we devise two auxiliary decoders, with the historical item sequence reconstruction task and the global item similarity prediction task, to further improve the reliability of user modeling. Experimental results on two datasets from simulated and real user search logs respectively show that the proposed personalized product search method outperforms existing approaches.

关键词： personalized product search sequential model graph

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