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2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)最新文献

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The Construction of Cascaded Features and Its Application in Fuzzy Clustering 级联特征的构造及其在模糊聚类中的应用
2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965479
Yin-Ping Zhao, Long Chen, C. L. P. Chen
{"title":"The Construction of Cascaded Features and Its Application in Fuzzy Clustering","authors":"Yin-Ping Zhao, Long Chen, C. L. P. Chen","doi":"10.1109/SPAC46244.2018.8965479","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965479","url":null,"abstract":"The success of fuzzy clustering heavily relies on the proper feature space constructed by the input data. For nonspherical and overlapped clusters, kernel fuzzy clustering is more effective owing to it finds more proper feature space compared to conventional fuzzy clustering. Unfortunately, poor scalability of kernel fuzzy clustering is induced by the requirement of large memory and running time. To solve the problem, random feature based method was presented to approximate the kernel function. Features in this approximate feature space are very useful information. Inspired by the architecture of functional-link neural network, to represent the diversity of features, cascaded features are constructed by a new feature mapping technique called cascaded feature mapping in this paper. By performing classical fuzzy c-means (FCM) with the cascaded features, a new fuzzy clustering algorithm called FCM-CF is developed. The experimental results of our proposed methods verify the superiority in comparison of other classical fuzzy clustering methods.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133438664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
IPSNN: Identification of Protein Structure based Neural Network 基于神经网络的蛋白质结构识别
2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965498
Hong-Xuan Hua
{"title":"IPSNN: Identification of Protein Structure based Neural Network","authors":"Hong-Xuan Hua","doi":"10.1109/SPAC46244.2018.8965498","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965498","url":null,"abstract":"Protein structures play key roles in many fields of biology. However, identification of protein structural types from protein sequences seems to be a challenge issue. In this work, protein structure prediction issue can be regarded as the peptide segments. The neural network, whose parameters is optimized by the PSO algorithm. And then, the sample can be described by the amino acid energy interaction, which is a novel feature, in this work. The results show that the IPSNN algorithm has better performances than other art-of-the-state methods.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132868865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Image retrieval method based on CNN and dimension reduction 基于CNN和降维的图像检索方法
2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965601
Zhihao Cao, Shaomin Mu, Yongyu Xu, Mengping Dong
{"title":"Image retrieval method based on CNN and dimension reduction","authors":"Zhihao Cao, Shaomin Mu, Yongyu Xu, Mengping Dong","doi":"10.1109/SPAC46244.2018.8965601","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965601","url":null,"abstract":"An image retrieval method based on convolution neural network and dimension reduction is proposed in this paper. Convolution neural network is used to extract high-level features of images, and to solve the problem that the extracted feature dimensions are too high and have strong correlation, multilinear principal component analysis is used to reduce the dimension of features. The features after dimension reduction are binary hash coded for fast image retrieval. Experiments show that the method proposed in this paper has better retrieval effect than the retrieval method based on principal component analysis on the e-commerce image datasets.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130990353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
An Adaptive Differential Evolution Algorithm Based on Fuzzy Modeling 一种基于模糊建模的自适应差分进化算法
2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965475
Dan-Ting Duan, Nankun Mu, X. Liao
{"title":"An Adaptive Differential Evolution Algorithm Based on Fuzzy Modeling","authors":"Dan-Ting Duan, Nankun Mu, X. Liao","doi":"10.1109/SPAC46244.2018.8965475","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965475","url":null,"abstract":"The appropriate parameter setting can substantially determine the performance of differential evolution (DE), so parameter design is a very crucial and challenging task in DE. In response to the realistic demands, a novel adjust strategy for adaptive parameter is developed for DE in this paper. By way of the strategy of fuzzy modeling, the phases of optimization are designed as follows, i.e., exploration, exploitation and convergence. The adaptive adjust of F and CR, the control parameters, is determined by the phases of optimization. Meanwhile, an auxiliary movement technique is designed for the convergence population. This technique will help the best individual to avoid the risk of falling into the potential local optima. The proposed algorithm, namely FMDE/rand/1, has been assessed under eight unimodal and multimodal benchmark functions. Results from experiments illustrate that the proposed FMDE/rand/1 is a promising optimization algorithm which will greatly enhance the performance on effectiveness and dynamic.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123205100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
License Plate Character Segmentation Using Key Character Location and Projection Analysis 基于关键字符定位和投影分析的车牌字符分割
2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965623
Bingshu Wang, C. L. P. Chen
{"title":"License Plate Character Segmentation Using Key Character Location and Projection Analysis","authors":"Bingshu Wang, C. L. P. Chen","doi":"10.1109/SPAC46244.2018.8965623","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965623","url":null,"abstract":"Character segmentation forms a link between license plate detection and character recognition. This paper presents a two-stage character segmentation framework. Firstly, a cascade classifier is trained by AdaBoost algorithm to locate key character which represents an administrative area. Then, based on key character location information, the rest of characters can be predicted or estimated. For the neighbor character of key character, it can be predicted by connected components analysis. For other characters that are far from the key character, a vertical projection strategy without hyphens is proposed to segment them. This strategy is promising to address the issue of touching characters. To illustrate the effectiveness of character segmentation, broad learning system is adopted to classify segmented characters. Experimental results performed on Macau license plates demonstrate the proposed method’s effectiveness in comparison with some state-of-the-art approaches. It is expected to apply the designed techniques to license plate character segmentation of other regions or countries that have similar situations with Macau’s.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125196136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Novel Image Quality Assessment and Enhancement Techniques for Finger Vein Recognition 手指静脉识别图像质量评估与增强新技术
2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965537
Kashif Shaheed, Lu Yang, Gongping Yang, Imran Qureshi, Yilong Yin
{"title":"Novel Image Quality Assessment and Enhancement Techniques for Finger Vein Recognition","authors":"Kashif Shaheed, Lu Yang, Gongping Yang, Imran Qureshi, Yilong Yin","doi":"10.1109/SPAC46244.2018.8965537","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965537","url":null,"abstract":"As a secure and reliable biometric trait, finger vein recognition (FVR) can be employed to verify the individuals in real-time applications. However, the pattern of vein is unclear in some finger vein images due to light scattering by the skin and non-uniform illumination, which deteriorates the performance of the FVR system. To deal with the image quality problem, a novel finger-vein image quality assessment method and an enhancement method are proposed. The proposed FVR Scheme is based on two folds: (i) Image Quality Assessment, and (ii) Image Enhancement. First, the quality of the image is assessed by the decision tree with r-smote technique, to classify the finger vein image into two classes, i.e. High Quality (HQ) and Low Quality (LQ) images. Second, a single scale retinex filter (SSR) with chromaticity preserved algorithm and Gaussian filter are proposed to enhance the low and high quality finger vein images. Total of 1052 finger vein images are employed for the testing aspect of quality evaluation, enhancement and recognition method. After that, low error rate EER of 0.0379 is obtained by the proposed art. Finally, the achieved results show the strength of the proposed art is better than already developed methods in FVR domain.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"13 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126288702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Facial expression recognition based on Gabor feature and neural network 基于Gabor特征和神经网络的面部表情识别
2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965443
Lei Pang, Nianqiang Li, Li Zhao, Wenxiu Shi, Yunpan Du
{"title":"Facial expression recognition based on Gabor feature and neural network","authors":"Lei Pang, Nianqiang Li, Li Zhao, Wenxiu Shi, Yunpan Du","doi":"10.1109/SPAC46244.2018.8965443","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965443","url":null,"abstract":"With the development of human-computer interaction, emotional computing has gradually become a hot issue in computer vision research. Human expressions contain a wealth of information. How to make the computer fully extract facial expression information and understand human emotions is an urgent problem to be solved. The difficulty of facial expression recognition lies in facial expression extraction and facial expression classification. This paper analyzes the local features of facial expression extracted by Gabor wavelet transform and performs the various dimensional reduction on the problem of feature redundancy, which balances the feature dimension and The contribution rate of the feature. After extracting the features, a suitable BP neural network is constructed, and the extracted features are trained as a data set to the neural network to obtain a good facial expression classifier.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125406398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Dividend payout and dual-class companies based on big data analysis 基于大数据分析的股利支付和双层股权结构公司
2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965604
Sai Qiao, Xiaorong Zhu
{"title":"Dividend payout and dual-class companies based on big data analysis","authors":"Sai Qiao, Xiaorong Zhu","doi":"10.1109/SPAC46244.2018.8965604","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965604","url":null,"abstract":"With the advent of the era of large data, facing massive data, we can apply statistical methods to mining the value hidden in the data.My study shows that there is a positive and significant relation between dividend payout and dual-class shares, suggesting that dual-class firms are more likely to pay dividend than single class firms. The finding is consistent with agency theory since agency theory implies that dividend can be used to mitigate the agency conflicts between managers and minority shareholders. I argue that this is because analyst coverage can effectively discipline the behaviors of managers so that analyst coverage can be used as alternative agency control mechanism of dividend to alleviate agency conflicts between managers and minority shareholders.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121755801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
End to End Background Subtraction by Deep Convolutional Neural Network 基于深度卷积神经网络的端到端背景减法
2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965532
Hongwei Sun
{"title":"End to End Background Subtraction by Deep Convolutional Neural Network","authors":"Hongwei Sun","doi":"10.1109/SPAC46244.2018.8965532","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965532","url":null,"abstract":"In this work, we use different methods from predecessors to perform background deductions, and our most important part still uses the network framework of CNN (deep convolution neural network). The advantage of this method is obvious. We can save the process of adjusting parameters and feature engineering. Because in the process of training a single video, it can learn structural network parameters from data. At the same time, we use the SUBSENSE structure to train data. It is a background structure that can monitor the moving objects in the video. It can better detect the foreground objects camouflaged and minimize the influence of unrelated factors such as illumination. Second, we use the long and short memory network (LSTM) to predict the significance of video, which can effectively avoid a limited number of training video over fitting, using our data through training successfully to learn the spatial and temporal significance of the estimation. Through our method, the final saliency prediction has achieved good results.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121832891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Bagging-based ensemble method for recommendations under uncertain rating data 不确定评级数据下基于bagging的推荐集成方法
2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2018-12-01 DOI: 10.1109/SPAC46244.2018.8965431
Ke Ji, Y. Yuan, R. Sun, Kun Ma, Zhenxiang Chen, Jian Liu
{"title":"A Bagging-based ensemble method for recommendations under uncertain rating data","authors":"Ke Ji, Y. Yuan, R. Sun, Kun Ma, Zhenxiang Chen, Jian Liu","doi":"10.1109/SPAC46244.2018.8965431","DOIUrl":"https://doi.org/10.1109/SPAC46244.2018.8965431","url":null,"abstract":"Matrix factorization (MF) is one of the most-used techniques to build recommender systems. However, in practical use, the existence of noise in the training set brings some uncertainty, degrading the performance of MF approaches. In this paper, we propose a Bagging-based MF framework, an ensemble method of using multiple MF-based models to improve the stability and accuracy. Specifically, our framework first rebuilds new training sets by resampling on the original ratings, then takes advantage of the sets to train MF models and finally combines the predictions from the models in a ensemble way. The experiment results on real data show that our framework can achieve some performance improvement when having noise samples.","PeriodicalId":360369,"journal":{"name":"2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114331888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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