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2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)最新文献

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Mining inter-transaction association rules from multiple time-series data 从多个时间序列数据中挖掘事务间关联规则
2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304268
Chunkai Zhang, Xudong Zhang, Z. L. Jiang, Qing Liao, Lin Yao, Xuan Wang
{"title":"Mining inter-transaction association rules from multiple time-series data","authors":"Chunkai Zhang, Xudong Zhang, Z. L. Jiang, Qing Liao, Lin Yao, Xuan Wang","doi":"10.1109/SPAC.2017.8304268","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304268","url":null,"abstract":"Association rule mining is one of the most widely used methods for discovering interesting relations between variables. Time series as a common sequence data have some unique character, such as pervasively connected, endless and time-related. Therefore research on multivariate time series data mining is a hot spot in data mining. This paper first compresses the continuous time series. Then in order to make the mining rules reflect the characteristics of multivariate time series data, our paper designs a new algorithm called IAMTL, which can mine the rules from the fix time span. For the reason that time series data have the characteristic of continuity, so an increment version of IATML is provided. At last, we use prerequisite and the consequent windows to verify the correctness of the rules.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122908610","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: 1
Estimation for piecewise homogeneous Markov jump systems: A dual hidden Markov model approach 分段齐次马尔可夫跳跃系统的估计:一种双重隐马尔可夫模型方法
2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304255
Mei Fang, Shanling Dong, Zhengguang Wu
{"title":"Estimation for piecewise homogeneous Markov jump systems: A dual hidden Markov model approach","authors":"Mei Fang, Shanling Dong, Zhengguang Wu","doi":"10.1109/SPAC.2017.8304255","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304255","url":null,"abstract":"The paper studies the asynchronous H, estimation problem for discrete-time piecewise nonhomogeneous Markov jump systems. An asynchronous estimator is designed by introducing a dual hidden Markov model, which is applied to track the plant mode. Based on the mode-dependent Lyapunnov function technique, a sufficient condition is developed to ensure that the filtering error system is stochastically stable with a prescribed H, performance index. Via Finsler's Lemma, a solution to the existence of H, estimator is obtained in terms of linear matrix inequalities. Finally, one example is provided to validate the effectiveness of our proposed approach.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114157503","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: 1
Shadow detection and removal for illumination consistency on the road 道路照明一致性的阴影检测和去除
2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304275
Chunxiang Wang, Liuyuan Deng, Zhiyu Zhou, Ming Yang, Bing Wang
{"title":"Shadow detection and removal for illumination consistency on the road","authors":"Chunxiang Wang, Liuyuan Deng, Zhiyu Zhou, Ming Yang, Bing Wang","doi":"10.1109/SPAC.2017.8304275","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304275","url":null,"abstract":"Shadows on the road always trouble vision tasks like visual navigation and road detection. Shadows will change road characteristics and occlude road objects. It is a great challenge to effectively detect and remove the shadows on the road to maintain illumination consistency for the vehicle. To tackle the adverse effect caused by shadows on the road, this paper attempts to detect shadows with Support Vector Machine (SVM) based on color saliency space and gradient field. Shadowed areas are distinguished and recognized by nonlinear SVM classifier through reconstructing road shadow descriptor after analyzing its color saliency space and gradient information. Then adaptive variable scale regional compensation operator is adopted to remove the shadows. Extensive experiments show that the shadow detection and removal method proposed in this paper has good feasibility and adaptability, and the method performs well under a variety of road environment.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"38 24","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120857865","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: 15
Double direction matrix based sparse representation for face recognition 基于双方向矩阵的稀疏表示人脸识别
2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304358
Jian-Xun Mi, Zhiheng Luo, Qiankun Fu, Ailian He
{"title":"Double direction matrix based sparse representation for face recognition","authors":"Jian-Xun Mi, Zhiheng Luo, Qiankun Fu, Ailian He","doi":"10.1109/SPAC.2017.8304358","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304358","url":null,"abstract":"Robust sparse representation is a well-known method in computer vision. Several sparse representation models have been proposed and perform well in face recognition. Most of them use transformed images of one dimensional vector, and such implementation ignores structural information between features. To make use of this structural information, this paper presents a novel model for face recognition, called double direction L2,1-norm based sparse representation. Unlike traditional sparse regression measuring differences between test sample and predicted response by vector norm, our model uses matrix norm, L2,1, to calculate residual. Instead of treating each pixel independently, the residual of a pixel is effected by all others in the same line and the same column by means of double direction L2,1-norm. And then, we use the alternating direction method of multipliers approach to optimize proposed model. Just as the L2,1-norm concerns, experiments show that our proposed method is more robust than other sparse methods.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125435461","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: 2
An ant colony optimization algorithm for three dimensional path planning 三维路径规划的蚁群优化算法
2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304341
Lanfeng Zhou, Weijie Qian, Guogang Cao
{"title":"An ant colony optimization algorithm for three dimensional path planning","authors":"Lanfeng Zhou, Weijie Qian, Guogang Cao","doi":"10.1109/SPAC.2017.8304341","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304341","url":null,"abstract":"The path planning problem of mobile robot in three dimension environment is studied in this paper. The initial pheromone of the algorithm is set. Considering the selection strategy of ant colony algorithm, a dynamic change relation of q0 is established by the number of iterations and path distance. The influence factor of path distance in heuristic function is introduced. In order to improve the randomness of route choice, the path selection rule has been improved. At the same time, in order to improve the convergence speed of the algorithm. The penalty mechanism of pheromone is adopted. Simulation results show that the length of the 3D path and the search efficiency are improved by the improved algorithm.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129952589","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: 4
A novel DE-PCCM feature for EEG-based emotion recognition 一种新的基于脑电图的情感识别DE-PCCM特征
2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304310
Hongyou Li, Chunmei Qing, Xiangmin Xu, T. Zhang
{"title":"A novel DE-PCCM feature for EEG-based emotion recognition","authors":"Hongyou Li, Chunmei Qing, Xiangmin Xu, T. Zhang","doi":"10.1109/SPAC.2017.8304310","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304310","url":null,"abstract":"Emotion recognition is a key work of research in Brain Computer Interactions. With the increasing concerns on affective computing, emotion recognition has attracted more and more attention in the past decades. Using electroencephalogra-phy(EEG) is a common way to distinguish emotions although it is also a challenging task. In this paper, we proposed a novel feature called DE-PCCM to improve the accuracy. The basic idea of DE-PCCM is to reveal the relationship between channels after extracting the differential entropy (DE) feature. The DE-PCCM feature can transform the DE features into 2D images so that it could be used as input of Convolutional neural network(CNN). In addition, we constructed a deep learning model for the DE-PCCM feature. Experiments are carried out on the SEED dataset, and our results demonstrate the superiority of the proposed method.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125456194","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
Adjacent graph-based block kernel nonnegative matrix factorization 基于邻接图的块核非负矩阵分解
2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304265
Wensheng Chen, Qian Wang, Binbin Pan
{"title":"Adjacent graph-based block kernel nonnegative matrix factorization","authors":"Wensheng Chen, Qian Wang, Binbin Pan","doi":"10.1109/SPAC.2017.8304265","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304265","url":null,"abstract":"Using block technique and graph theory, we present a variant of nonnegative matrix factorization (NMF) with high performance for face recognition. We establish a novel objective function in kernel space by the class label information and local scatter information. The class label information is implied in the block decomposition technique and intra-class covariance matrix, while the local scatter information is determined by the adjacent graph matrix. We theoretically construct an auxiliary function related to the objective function and then derive the iterative formulae of our method by solving the stable point of the auxiliary function. The property of auxiliary function shows that our algorithm is convergent. Finally, empirical results show that our method is effective.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128396110","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: 1
A multi-kernel based Gaussian process dynamic model for human motion modeling 基于多核高斯过程的人体运动建模
2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304322
Ziqi Zhu, Jiayuan Zhang, Jixin Zou
{"title":"A multi-kernel based Gaussian process dynamic model for human motion modeling","authors":"Ziqi Zhu, Jiayuan Zhang, Jixin Zou","doi":"10.1109/SPAC.2017.8304322","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304322","url":null,"abstract":"In this paper, we focus on the problem of human motion modeling. We adopt the probabilistic modeling approach to over come the over-fitting problem in the parameter training process and propose a multi-kernel based Gaussian process dynamic model. First, we will do the dimensional reduction, and the method is the Gaussian process latent variable model. Different from existing modeling method, we introduce multikernel learning into the dimensional reduction process to capture the complex distribution of high-dimensional data. Second, for modeling the dynamic latent variable, we use a multi-kernel learning. We are not give a strong assumption on form of the nonlinear projection mapping and nonlinear dynamic function, our model automatically learn a suitable nonlinear kernel based on the training samples, and it can fit many kind of times series. We demonstrate the effectiveness of our method on the CMU human motion data set. The Experimental results show that our modeling method achieves promising modeling capability and is capable of predict human motion.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130336520","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: 1
Hierarchical learning for salient object detection 显著目标检测的分层学习
2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304274
Qi Zheng, Peng Zhang, Xinge You, Fangzhao Wang, Zida Liu
{"title":"Hierarchical learning for salient object detection","authors":"Qi Zheng, Peng Zhang, Xinge You, Fangzhao Wang, Zida Liu","doi":"10.1109/SPAC.2017.8304274","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304274","url":null,"abstract":"Most existing methods for salient object detection either depend on simple feature, such as contrast or boundary prior, which is sensitive to background variety, or extract redundant features for robustness, which is time-consuming. In this paper, we propose a hierarchical learning structure to alleviate the demanding feature. The hierarchical learning is based on low-rank (LR) decomposition and broad learning system (BLS). LR model with Laplacian constraint is applied to roughly separate foreground from background, which produces several positive and negative super-pixels as example to train a BLS classifier. The classifier is used to determine the final saliency of each superpixel. Experiments on two public datasets including MSRA10K and ECSSD show that our method achieves state-of-the-art result compared with the other nine methods.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134593823","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
Learning a discriminative feature for object detection based on feature fusing and context learning 学习一种基于特征融合和上下文学习的判别特征用于目标检测
2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304337
You Lei, Hongpeng Wang, Y. Wang
{"title":"Learning a discriminative feature for object detection based on feature fusing and context learning","authors":"You Lei, Hongpeng Wang, Y. Wang","doi":"10.1109/SPAC.2017.8304337","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304337","url":null,"abstract":"Object detection is one of the most challenging tasks in the field of computer vision. It is widely used in traffic sign detection[1], pedestrian detection[2,3], person re-identification[4], object tracking[5,6,7] and so on[8,9]. Although convolutional neural network (CNN)-based algorithms have made great achievements in this field, object detection still suffers from illumination changes, occlusion, intraclass differences, etc.[10]. Candidate bounding box generation methods and feature extraction methods also influence the final detection result. In this paper, we propose a discriminative feature extraction method based on feature fusion and context learning.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130930622","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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