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2014 International Conference on information Engineering and Education Science

作者： Yanrong Shi School of Computer Science and Technology Shandong Institute of Business and TechnologyYantaiChina Key Laboratory of Intelligent Information Processing in Universities of ShandongShandong Institute of Business and Technology

The computer experiment teaching is a necessary step to verify the theory of classroom teaching,is an important way to cultivate students' practical ability,innovation ability and team spirit of *** at the existing problems in the computer experiment teaching,we carried out to study and practice the teaching reform of computer experiments from several aspects,and achieved certain results.

关键词： Computer experiment teaching College teaching reform Different levels

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AgeNet: Deeply Learned Regressor and Classifier for Robust Apparent Age Estimation

AgeNet: Deeply Learned Regressor and Classifier for Robust A...

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Xin Liu Shaoxin Li Meina Kan Jie Zhang Shuzhe Wu Wenxian Liu Hu Han Shiguang Shan Xilin Chen Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Beijing China Tencent BestImage Team Shanghai China

Apparent age estimation from face image has attracted more and more attentions as it is favorable in some real-world applications. In this work, we propose an end-to-end learning approach for robust apparent age estimation, named by us AgeNet. Specifically, we address the apparent age estimation problem by fusing two kinds of models, i.e., real-value based regression models and Gaussian label distribution based classification models. For both kind of models, large-scale deep convolutional neural network is adopted to learn informative age representations. Another key feature of the proposed AgeNet is that, to avoid the problem of over-fitting on small apparent age training set, we exploit a general-to-specific transfer learning scheme. Technically, the AgeNet is first pre-trained on a large-scale web-collected face dataset with identity label, and then it is fine-tuned on a large-scale real age dataset with noisy age label. Finally, it is fine-tuned on a small training set with apparent age label. The experimental results on the ChaLearn 2015 Apparent Age Competition demonstrate that our AgeNet achieves the state-of-the-art performance in apparent age estimation.

关键词： Estimation Encoding Robustness Neural networks Face Aging Standards

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A saliency detection method based on global contrast

International Journal of Signal Processing, Image Processing...

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International Journal of Signal processing, Image processing and Pattern Recognition 2015年第7期8卷 111-122页

作者： Yu, Chun-Yan Zhang, Wei-Shi Wang, Chun-Li Institute of Information and Science Technology Dalian Maritime University China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University No.3 Feixi Road Hefei China

To highlight the saliency object clearly from the foreground, we propose a saliency detection method based on global contrast with cluster. Due to the fact that background pixels usually have similar patches, we use cluster analysis to merge the background regions. By using mean shift filter, the background pixels with similar color level are clustered and the saliency calculation can be decreased a lot. In the method, we use the contrast of color feature with all the other pixels to compute the saliency map. A weight coefficient is utilized to improve the detection accuracy in global contrast differences evaluation. The results of extensive experiments on public dataset show that our method perform well and can highlight the salient object clearly against the other five state-of-the-art methods. Besides, we demonstrate that the applications in image segmentation and fusion with our saliency map can get satisfactory results. © 2015 SERSC.

关键词： Image segmentation

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Protein model quality assessment by learning-to-rank

Protein model quality assessment by learning-to-rank

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IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015

作者： Jing, Xiaoyang Dong, Qiwen Liu, Xuan Liu, Bin School of Computer Science Fudan University Shanghai200433 China Shanghai Key Laboratory of Intelligent Information Processing Shanghai200433 China College of Engineering Shanghai Ocean University Shanghai201303 China School of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate School Shenzhen Guangdong518055 China

ISBN: (纸本)9781467367981

Protein structures are essential to understand the function. The predicted models have a broad range of the accuracy. Reliable estimates of the model quality are critical in determining the usefulness of the model to address a specific problem. In this study, a novel method has been presented to rank the models by their relative qualities. The proposed method first extracts various features from the three dimensional structures of proteins and then the learning-to-rank algorithm is used to rank the models based on their similarities with the native structures. Furthermore, a quasi single-model method is presented, which uses the top five identified models as references and ranks the other models by the average similarity with the reference models. Benchmark test is performed on a newly developed, template-based decoy generators which covers all the main structure classes of proteins. The proposed learning-to-rank method achieves an average Pearson correlation coefficient of 0.94 and a AUC value of 0.97, which consistently outperform all other well-developed methods. The quasi single-model can further improves the performance and achieve nearly perfect results with both PCC and AUC value of 0.99. The results demonstrate that the proposed method is an effective methodology for model quality assessment and provides the state-of-the-art performance. © 2015 IEEE.

关键词： Proteins

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A novel level set model based on multi-scale local structure operation for texture image segmentation

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Journal of information and Computational Science 2015年第1期12卷 9-20页

作者： Min, Hai Wang, Xiaofeng Department of Automation University of Science and Technology of China Hefei China Intelligent Computing Lab Hefei Institute of Intelligent Machines Chinese Academy of Sciences Hefei China Key Lab of Network and Intelligent Information Processing Department of Computer Science and Technology Hefei University Hefei China

Regional level set method is a popular approach for image segmentation that uses inside and outside information of contour to extract object boundary. Unfortunately, in many cases, such method is not adequate to model complex textured objects. In this paper, we propose a natural texture image segmentation method which incorporates the pixel-level feature into region-level feature. The multi-scale local structure operation is proposed as pixel-level feature to describe the texture structure of image. So the problems of multi-scale and rotation invariance of inhomogeneous texture are addressed by introducing multi-scale local structure operation into level set energy functional. Then, the global intensity information is extracted as the region-level feature and integrated with multi-scale local structure operation. Further, we propose a so-called vector level set method to obtain the segmentation results. Here, we extend the traditional regional level set model into the vector formulation so that the multi-scale local structure operation can be suitably combined with the global intensity information to achieve the more superior image segmentation performance than that of the traditional segmentation methods for texture images. Experiments on some synthesis texture images and real natural scene images demonstrate the excellent performance of the proposed method which successfully combines local structure information and global intensity information to extract the object boundary.

关键词： Image segmentation

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Prediction of negative conversion days of childhood nephrotic syndrome based on the improved backpropagation neural network with momentum

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International Journal Bioautomation 2015年第4期19卷 543-554页

作者： Liu, Yijun Wang, Beihong Tang, Jiali Zhu, Mingfang Chen, Dan Jiang, Hongfen Chen, Xiangjun Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology Zhongwu Road 1801 Changzhou213001 China Department of Pediatrics Changzhou No.2 People's Hospital Xinglong Road 29 Changzhou213003 China Guangxi Higher Education Key Laboratory of Science Computing and Intelligent Information Processing Guangxi Teachers Education University Yanziling Road 4 Nanning530023 China

Childhood nephrotic syndrome is a chronic disease harmful to growth of children. Scientific and accurate prediction of negative conversion days for children with nephrotic syndrome offers potential benefits for treatment of patients and helps achieve better cure effect. In this study, the improved backpropagation neural network with momentum is used for prediction. Momentum speeds up convergence and maintains the generalization performance of the neural network, and therefore overcomes weaknesses of the standard backpropagation algorithm. The three-tier network structure is constructed. Eight indicators including age, lgG, lgA and lgM, etc. are selected for network inputs. The scientific computing software of MATLAB and its neural network tools are used to create model and predict. The training sample of twenty-eight cases is used to train the neural network. The test sample of six typical cases belonging to six different age groups respectively is used to test the predictive model. The low mean absolute error of predictive results is achieved at 0.83. The experimental results of the small-size sample show that the proposed approach is to some degree applicable for the prediction of negative conversion days of childhood nephrotic syndrome.

关键词： Momentum

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Exploiting Feature Hierarchies with Convolutional Neural Networks for Cultural Event Recognition

Exploiting Feature Hierarchies with Convolutional Neural Net...

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Mengyi Liu Xin Liu Yan Li Xilin Chen Alexander G. Hauptmann Shiguang Shan Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Beijing China School of Computer Science Carnegie Mellon University USA

Cultural events are kinds of typical events closely related to history and nationality, which play an important role in cultural heritage through generations. However, automatically recognizing cultural events still remains a great challenge since it depends on understanding of complex image contents such as people, objects, and scene context. Therefore, it is intuitive to associate this task with other high-level vision problems, e.g., object detection, recognition, and scene understanding. In this paper, we address this problem by combining both ideas of object / scene contents mining and strong image representation via CNN into a whole framework. Specifically, for object / scene contents mining, we employ selective search to extract a batch of bottom-up region proposals, which are served as key object / scene candidates in each event image, while for representation via CNN, we investigate two state-of-the-art deep architectures, VGGNet and GoogLeNet, and adapt them to our task by performing domain-specific (i.e., event) fine-tuning on both global image and hierarchical region proposals. These two models can complementarily exploit feature hierarchies spatially, which simultaneously capture the global context and local evidences within the image. In our final submission for ChaLearn LAP Challenge ICCV 2015, nine kinds of features extracted from five different deep models were exploited and followed with two kinds of classifiers for decision level fusion. Our method achieves the best performance of mAP=0.854 among all the participants in the track of cultural event recognition.

关键词： Cultural differences Feature extraction Proposals Image recognition Visualization Search problems Neural networks

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Study of automated PCNN system based on fruit fly optimization algorithm

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Journal of Computational information Systems 2014年第15期10卷 6635-6642页

作者： Zhao, Han Ding, Shifei School of Computer Science and Technology China University of Mining and Technology Xuzhou China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

As the third-generation neural network technology, pulse coupled neural network (PCNN) had used in many fields successfully, but it hindered its popularize that so many parameters of the PCNN need to be set up. This paper presented a new method to automatically determine the parameters of the PCNN based on Fruit Fly Optimization Algorithm (FOA). In proposed method, the information entropy of the image is defined as the fitness function of Fruit Fly Optimization Algorithm when searching the optimal solution. Mutual information is used as the evaluation criteria in the simulation experiments. Contrasted the simulation result with the experiments used the Otsu method, manual adjustment method, genetic algorithm method and PSO method, it shows that the FOA method is better than the others. © 2014 Binary information Press

关键词： Image segmentation

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Firefly algorithm with generalised opposition-based learning

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International Journal of Wireless and Mobile computing 2015年第4期9卷 370-376页

作者： Wang, Hui Wang, Wenjun Sun, Hui School of Information Engineering Nanchang Institute of Technology Nanchang330099 China Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang330099 China School of Business Administration Nanchang Institute of Technology Nanchang330099 China

Firefly Algorithm (FA) is a new optimisation algorithm based on swarm intelligence, which has shown good performance on many optimisation problems. However, the standard FA easily falls into local minima because of too many attraction operations. To enhance the performance of the standard FA, a new FA is proposed in this paper. The new approach employs Generalised Opposition-Based Learning (GOBL) for population initialisation and generation jumping. To verify the performance of our approach, a set of benchmark functions tested in the experiments. Computational results show that the proposed approach obtains better performance than the standard FA and some recently proposed FA variants. © Copyright 2015 Inderscience Enterprises Ltd.

关键词： Global optimization

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RETRACTED: Dual-mode predictive flexible rotor control for active magnetic bearings with input constraints

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Journal of Vibration and Control 2016年第20期23卷 NP2页

作者： Yue Wu Jie Zhao Hai-Tao Zhang Ming-Can Fan Xi Yu Guangdong HUST Industrial Technology Research Institute Guangdong Province Key Lab of Digital Manufacturing Equipment ChinaKey Laboratory of Image Processing and Intelligent Control School of Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology China Key Laboratory of Image Processing and Intelligent Control School of Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology China Department of Mathematics Huizhou University China School of Electrical and Information Engineering Xi'an Technology University China

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