2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)最新文献_第6页

Face liveness detection using color gradient features 基于颜色梯度特征的人脸活动性检测

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304308

Jixiang Dong, Chunwei Tian, Yong Xu

引用次数: 6

Feature selection for high dimensional imbalanced class data based on F-measure optimization 基于f -测度优化的高维不平衡类数据特征选择

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304290

Chunkai Zhang, Guoquan Wang, Ying Zhou, Lin Yao, Z. L. Jiang, Qing Liao, Xuan Wang

{"title":"Feature selection for high dimensional imbalanced class data based on F-measure optimization","authors":"Chunkai Zhang, Guoquan Wang, Ying Zhou, Lin Yao, Z. L. Jiang, Qing Liao, Xuan Wang","doi":"10.1109/SPAC.2017.8304290","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304290","url":null,"abstract":"Feature selection is designed to eliminate redundant attributes and improve classification accuracy. This is a challenging problem, especially in the case of imbalanced data. The traditional feature selection methods ignores the problem of class imbalance, making the selected features biased towards the majority class and neglecting the significant features for the minority class. Due to the advantage of F-measure in imbalanced data classification, we propose to use F-measure rather than accuracy as the optimization target in feature selection algorithm. This paper introduces a novel feature selection method SSVM-FS which is based on an optimal F-measure structural support vector machine classifier. Features will be selected according to the weight vector of SSVM which takes class imbalance problem into account. Based on this, we developed a comprehensive feature ranking method which integrate weight vector of SSVM and symmetric uncertainty. We use the comprehensive score to reduce the features to a suitable size and then use a harmony search to find the optimal combination of features to predict the target class label. The feature subset selected by the proposed method can represent both majority and minority class, in addition, it is less redundant. The experimental results on six high dimensional class imbalanced microarray data sets show that this method is a better method to solve the unbalanced classification.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"54 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":"130129915","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: 14

Zero-shot image classification based on attribute 基于属性的零拍图像分类

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304245

Wei Zhang, Wenbai Chen, Xiangfeng Chen, Hu Han

{"title":"Zero-shot image classification based on attribute","authors":"Wei Zhang, Wenbai Chen, Xiangfeng Chen, Hu Han","doi":"10.1109/SPAC.2017.8304245","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304245","url":null,"abstract":"In the image classification task, traditional model can only recognize annotated image samples, but class labels can't involve all the object categories. In order to reduce the dependence on the labels and recognize unannotated object samples, this paper proposes zero-shot image classification based on attribute. The binary attribute is used as the intermediate knowledge to migrate learned knowledge from training samples domain to test samples domain. Using the classification model of the multi-loss function based on ResNet-50 to predict the object attributes. Then, using an attribute matrix to represent the correspondence between the object class and the attribute. Finally, the result of attribute prediction is combined with the prior knowledge of the attribute matrix to get the category. Compared with the traditional image classification method, the attribute learning model is applied to the zero-shot image classification. The experimental data show that the method improves the recognition accuracy of the image and improves the flexibility of the image classification task, which lays the foundation for the multi-source domain adaptation induction problem.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"4 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":"114929780","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

Convolutional neural networks based scale-adaptive kernelized correlation filter for robust visual object tracking 基于卷积神经网络的尺度自适应核相关滤波鲁棒视觉目标跟踪

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304316

B. Liu, Zhengyu Zhu, Yong Yang

{"title":"Convolutional neural networks based scale-adaptive kernelized correlation filter for robust visual object tracking","authors":"B. Liu, Zhengyu Zhu, Yong Yang","doi":"10.1109/SPAC.2017.8304316","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304316","url":null,"abstract":"Visual object tracking is challenging when the object appearances occur significant changes, such as scale change, background clutter, occlusion, and so on. In this paper, we crop different sizes of multiscale templates around object and input these multiscale templates into network to pretrain the network adaptive the size change of tracking object. Different from previous the tracking method based on deep convolutional neural network (CNN), we exploit deep Residual Network (ResNet) to offline train a multiscale object appearance model on the ImageNet, and then the features from pretrained network are transferred into tracking tasks. Meanwhile, the proposed method combines the multilayer convolutional features, it is robust to disturbance, scale change, and occlusion. In addition, we fuse multiscale search strategy into three kernelized correlation filter, which strengthens the ability of adaptive scale change of object. Unlike the previous methods, we directly learn object appearance change by integrating multiscale templates into the ResNet. We compared our method with other CNN-based or correlation filter tracking methods, the experimental results show that our tracking method is superior to the existing state-of-the-art tracking method on Object Tracking Benchmark (OTB-2015) and Visual Object Tracking Benchmark (VOT-2015).","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"303 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":"114588246","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 diffusion optical model for skin scattering suppression in finger vein image restoration 手指静脉图像恢复中皮肤散射抑制的扩散光学模型

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304368

Wenhao You, Weikang Zhou, Jing Huang, Yaqin Liu, Feng Yang, Ziyu Chen

引用次数: 1

A symbolic representation approach of EEG signals for emotion recognition 一种用于情绪识别的脑电信号符号表示方法

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304359

Jiachen Du, Ruifeng Xu, Zhiyuan Wen

{"title":"A symbolic representation approach of EEG signals for emotion recognition","authors":"Jiachen Du, Ruifeng Xu, Zhiyuan Wen","doi":"10.1109/SPAC.2017.8304359","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304359","url":null,"abstract":"Emotion recognition based on electroencephalogram (EEG) signals provides a direct access to inner state of a user, which is considered an important factor in Human-Machine-Interaction (HMI). Traditional feature extraction methods for EEG signals always suffer from high dimension and unsatisfactory interpretability. In this paper, we propose a novel symbolic representation approach of EEG signals for emotion recognition. By applying the Symbolic Aggregate approXimation(SAX) algorithm, the continuous EEG signals are represented as discrete symbol strings. The bag of words model and Latent Semantic Indexing algorithm are then performed to extract and select the word features from the symbolic strings as the discriminative features in a Support Vector Machine based classifier for emotion classification. The evaluations on DEAP dataset show that our proposed approach outperforms the three typical methods stably. Meanwhile, the symbolic representation is shown helpful to improve the interpretability of similar EEG signals. The more important issue is that this approach brings a new way to represent the EEG signal. It is helpful to introduce the natural language processing techniques to EEG signal analysis and classification research.","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":"126069257","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 novel density peak clustering algorithm based on squared residual error 一种新的基于残差平方的密度峰聚类算法

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304248

M. Parmar, D. Wang, A. Tan, C. Miao, Jianhua Jiang, You Zhou

引用次数: 15

A novel privacy-preserving distributed anomaly detection method 一种新的保护隐私的分布式异常检测方法

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304323

Chunkai Zhang, Haodong Liu, Ye Li, Ao Yin, Z. L. Jiang, Qing Liao, Xuan Wang

引用次数: 1

Social personalized ranking recommendation algorithm by trust 基于信任的社会个性化排名推荐算法

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304289

Gai Li, Youfen Chen, Zhiqiang Zhang, Jianghong Zhong, Weihua Ou

引用次数: 0

Optimized generalized hough transform for road marking recognition application 优化广义霍夫变换在道路标志识别中的应用

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304305

Bin Fang, Lisi Qian

{"title":"Optimized generalized hough transform for road marking recognition application","authors":"Bin Fang, Lisi Qian","doi":"10.1109/SPAC.2017.8304305","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304305","url":null,"abstract":"The road markings recognition is an important research in the field of driverless cars. The generalized Hough transform (GHT) is effective for detecting and recognizing contour objects as road markings. While the precision rate of GHT is not very high in applications. This paper presents an edge-type based generalized Hough transform (ETGHT). The edge-type is obtained by multiple thresholds partition of a proposed edge feature and is recorded by multiple R-tables. The edge feature is calculated by a breadth first search strategy using the location and gradient direction of the edge points. In application, a road marking recognition framework based on ETGHT is presented. First, an edge extraction method based on differential excitation is used to obtain the image contours. Then the edge-type feature of the edge points of input image is extracted to determine the corresponding R-table. In the voting stage, a peak region screening processing is used to improve the system's precision rate. Experimental results have shown that the proposed method provides significant improvement of precision rate while ensuring the recall rate.","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":"124799604","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