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

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Shadowed set-based rough-fuzzy clustering using random feature mapping 基于随机特征映射的阴影集粗模糊聚类
2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304312
Lingning Kong, Long Chen
{"title":"Shadowed set-based rough-fuzzy clustering using random feature mapping","authors":"Lingning Kong, Long Chen","doi":"10.1109/SPAC.2017.8304312","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304312","url":null,"abstract":"The shadowed set-based rough fuzzy clustering (SRFCM) methods have shown great performance on the data with outliers. But for the data with non-spherical clusters, the SRFC approaches cannot produce good results. The reason is the SRFCM, just like classical fuzzy c-means algorithms, works on the original data space and assures the linear separability of different clusters. The kernel methods can be combined with fuzzy clustering to deal with the non-spherical problem, but the size of kernel matrix is the square of the number of the input data, which makes the kernel fuzzy clustering is not suitable for very large data. But if we approximate the kernel space by using Fourier random feature mappings, the SRFC can be directly applied over the random features generated by data. This approach combines the advantages of SRFCM in handling outliers and the random features in processing non-spherical clusters. The experimental results show good performance of the SRFCM in the random feature space.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"82 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":"121373866","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
Accurate segmentation of Ulva prolifera regions with superpixel and CNNs 利用超像素和cnn对增生Ulva区域进行精确分割
2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304318
Shengke Wang, Lu Liu, Lianghua Duan, Changyin Yu, Guiyan Cai, Feng Gao, Junyu Dong
{"title":"Accurate segmentation of Ulva prolifera regions with superpixel and CNNs","authors":"Shengke Wang, Lu Liu, Lianghua Duan, Changyin Yu, Guiyan Cai, Feng Gao, Junyu Dong","doi":"10.1109/SPAC.2017.8304318","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304318","url":null,"abstract":"To get regions of Ulva prolifera, we propose a novel end-to-end way to segment the Ulva prolifera regions via aggregation of local classify prediction results. We creatively adopt SEEDS (Superpixels Extracted via Energy-Driven Sampling) to generate local multi-scale patches. We use powerful convolution neural networks to learn and classify the patches. At last, mapping the classify prediction results of patches to the whole image according to the patches classify prediction results, we can get more detailed segmentation of Ulva prolifera. As for the dataset, we collected images by UAV (unmanned aerial vehicle) in coastal waters off Qingdao, China. We show experimentally this method achieves great segmentation performance of Ulva prolifera, despite its indistinct features. In contrast, we train the model in fully convolutional networks for semantic segmentation based on our dataset, while our result achieves superior accuracy.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"12 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":"122753578","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
Sweat glands extraction in optical coherence tomography fingerprints 光学相干断层扫描指纹的汗腺提取
2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304344
Shuang Sun, Zhenhua Guo
{"title":"Sweat glands extraction in optical coherence tomography fingerprints","authors":"Shuang Sun, Zhenhua Guo","doi":"10.1109/SPAC.2017.8304344","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304344","url":null,"abstract":"Optical coherence tomography (OCT) is a non-invasive technique which can capture high-resolution three-dimension fingerprints. The surface fingerprint, sweat glands and internal fingerprint in OCT fingerprint provides more information than conventional two-dimension fingerprints. In this paper, we present a sweat gland extraction method. The method detects each gland's position using Frangi's filter and segment them by thresholding method. The experiment result on 50 fingers shows our method can successfully segment sweat glands. We also scanned fake fingerprints to do liveness detection. Different from real fingers, fake fingerprints do not have internal structures. We can distinguish fake fingerprints easily with OCT fingerprints. We also show that combining sweat glands information with fingerprint valleys and ridges can improve performance of fingerprint identification.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"15 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":"128555666","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: 13
An approach for learning the optimal “tuned” masks based on differential evolution algorithm 基于差分进化算法的最优“调谐”掩模学习方法
2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304345
Xu Zhang, Z. Ye, Juan Yang, W. Liu, Huazhong Jin
{"title":"An approach for learning the optimal “tuned” masks based on differential evolution algorithm","authors":"Xu Zhang, Z. Ye, Juan Yang, W. Liu, Huazhong Jin","doi":"10.1109/SPAC.2017.8304345","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304345","url":null,"abstract":"Texture image classification is a significant topic in many applications of machine vision and image analysis. The texture feature extracted from the original image by using the “Tuned” mask is one of the simplest and most effective methods. However, the primary gradient based training method almost always falls into the local optimum which might be improved through some commonly used evolutionary algorithms, such as genetic algorithm (GA) and particle swarm optimization (PSO). Unfortunately, these algorithms will easily trap into the local optimum as well. For the sake of learning “Tuned” mask with the better performance, this paper propose to employ differential evolution algorithm to generate the optimal “Tuned” mask. Experiments on some texture images from the Brodatz album show that the “Tuned” mask training method proposed in this paper is very effective for classifying texture images and outperforms the “Tuned” mask training method based on genetic algorithm and particle swarm optimization algorithm.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"17 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":"128744731","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
Abnormal behavior detection for harbour operator safety under complex video surveillance scenes 复杂视频监控场景下港口经营人安全异常行为检测
2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304298
Guoan Cheng, Shengke Wang, Teng Guo, Xiao Han, Guiyan Cai, Feng Gao, Junyu Dong
{"title":"Abnormal behavior detection for harbour operator safety under complex video surveillance scenes","authors":"Guoan Cheng, Shengke Wang, Teng Guo, Xiao Han, Guiyan Cai, Feng Gao, Junyu Dong","doi":"10.1109/SPAC.2017.8304298","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304298","url":null,"abstract":"In this paper, we analyze the problems faced by current video surveillance systems in the safety detection of port operators, and systematically research the methods of information extraction and operator behavior warnings. We collect a large number of port operation videos, and establish a large-scale port operations scene dataset named Harbor Dataset. We propose a deep learning based object detection algorithm to carry out the safety detection of the operator in the harbor scene. By comparing the detected personnel position and the location of the calibration area, we can judge the violation of the operator. We also use real-time tracking and data retention of offending operators to reduce the operation of illegal operators. Experiments show that our method yields a competitive result on Harbor Dataset in detecting the safety of the operating personnel and calibrating the dangerous operation areas.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"21 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":"126231439","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
An unsupervised abnormal crowd behavior detection algorithm 一种无监督异常人群行为检测算法
2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304279
Fanchao Xu, Yunbo Rao, Qifei Wang
{"title":"An unsupervised abnormal crowd behavior detection algorithm","authors":"Fanchao Xu, Yunbo Rao, Qifei Wang","doi":"10.1109/SPAC.2017.8304279","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304279","url":null,"abstract":"In this paper, we propose a detection algorithm based on people counting for two special kinds of abnormal crowd behavior, gathering and dispersing. We use an efficient foreground segmentation algorithm for calculating the number of people, which uses an approximate median filter and double background model to obtain a reliable foreground. Further, counting people globally based on potential energy model in crowd scenes. In order to detecting unnormal crowd behavior happened, a crowd distribution curve is proposed, which combines results of counting and crowd entropy to evaluate the spatial distribution of throng, and describes the global distribution as a good feature. Experiments prove that our proposed method is able to detect the abnormal crowd behavior efficiently without camera calibration or supervised training.","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":"128534402","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: 9
A novel asymmetric semantic similarity measurement for semantic job matching 一种新的语义任务匹配的非对称语义相似度测量方法
2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304267
Bo Zhu, Xin Li, Jesus Bobadilla Sancho
{"title":"A novel asymmetric semantic similarity measurement for semantic job matching","authors":"Bo Zhu, Xin Li, Jesus Bobadilla Sancho","doi":"10.1109/SPAC.2017.8304267","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304267","url":null,"abstract":"Applying semantic similarity techniques in semantic matching applications can help to match information not only lexically but also semantically. In this paper, we extend the conventional semantic similarity measures for retrieving and ranking employment candidates in the case of semantic job matching. A framework for calculating asymmetric conceptual skill similarity is proposed, and validated in a use case of programming job matching. Within this case, a specific skills taxonomy has been formalized in Simple Knowledge Organization System (SKOS). A novel asymmetric semantic similarity measurement based on weighted-path-counting is proposed and validated in the use case. The proposed algorithms are evaluated by comparing them to user ranks, and our experimental results show that the proposed algorithms have better performance in ranking comparing to the conventional algorithms.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"13 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":"117208467","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
Graph embedding and its application in defect detection system 图嵌入及其在缺陷检测系统中的应用
2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304363
Qifeng Huang, A. Zheng, Xuesong Shao, Xiaoquan Lu, Meimei Duan
{"title":"Graph embedding and its application in defect detection system","authors":"Qifeng Huang, A. Zheng, Xuesong Shao, Xiaoquan Lu, Meimei Duan","doi":"10.1109/SPAC.2017.8304363","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304363","url":null,"abstract":"Recent years, auto meter reading system performance has been the focus of research. In this paper, we apply graph analysis method to embedding the node in the power system to detect the defect in auto meter reading system. We use decision tree algorithm to determine the problematic nodes. We tried five kinds of graph embedding methods to experiment. We find that these methods have improved the accuracy of fault diagnosis to a certain extend.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"778 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":"133842670","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
Rank learning algorithm for user reputation 用户信誉等级学习算法
2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304369
Yuxin Ding, Ling Xie, Zhensheng Kang, Zhiyang Song
{"title":"Rank learning algorithm for user reputation","authors":"Yuxin Ding, Ling Xie, Zhensheng Kang, Zhiyang Song","doi":"10.1109/SPAC.2017.8304369","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304369","url":null,"abstract":"User reputation systems are widely used in Ecommerce website and social networks. In present most of the user reputation systems use the rule-based method or the voting systems to calculate user reputations. These systems heavily depend on the experience of experts. In this paper we try to use machine learning method to automatically learn user reputation in social networks. The social network we selected is a financial forum. A social network is seen as a directed graph, every user in the networks is a node in the graph, and the interactions between the users are the directed edges. Then we extract features of users from the social network graph. We translate the reputation learning problem into the document ranking problem, and use the listwise based rank learning method to build the reputation model. The reputation prediction model is represented as a linear model. We use the model to predict user reputation. The experimental results show that using rank learning method to predict user reputation is effective.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"44 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134288866","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
People counting based on improved gauss process regression 基于改进高斯过程回归的人口计数
2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304348
Wenju Li, Yunfan Lu, Jingyi Sun, Qi Chen, T. Dong, Lanfeng Zhou, Qing Zhang, Lihua Wei
{"title":"People counting based on improved gauss process regression","authors":"Wenju Li, Yunfan Lu, Jingyi Sun, Qi Chen, T. Dong, Lanfeng Zhou, Qing Zhang, Lihua Wei","doi":"10.1109/SPAC.2017.8304348","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304348","url":null,"abstract":"Ideally, in the method about people counting based on multi-feature regression, the features, such as weighted blob area and perimeter, should have a linear relationship with the number of people in the scene. However, although the overall linear trend, due to the existence of occlusion, the foreground extraction errors and other factors, the local presents nonlinear characteristics. Gauss process regression is very suitable for linear features with local nonlinearity, so it is widely used at present to achieve the regression analysis between the features and the number of people using the Gauss process regression. In order to obtain higher accuracy, based on the research of the insufficient of the traditional Gauss process regression method, an improved Gauss process regression method is proposed to people counting. The experimental results show that the proposed method can get better performance. Firstly, the foreground blob and features of image sequences are extracted. Next, the square exponential covariance function is selected as kernel function. The bacterial foraging algorithm is used to optimize the hyper-parameters to obtain the optimal solution, and then the regression model is established. The experimental results show that the proposed algorithm which makes use of bacterial foraging to optimize the hyper-parameters can obtain better parameters and improve the accuracy of the people counting.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"185 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":"121090824","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: 6
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