2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)最新文献_第5页

Ransomware detection based on V-detector negative selection algorithm 基于v -检测器负选择算法的勒索软件检测

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304335

Tianliang Lu, Lu Zhang, Shunye Wang, Qi Gong

引用次数: 23

Distracter-aware correlation filter tracking 干扰感知相关滤波器跟踪

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304244

Xiaohuan Lu, Zhenyu He

{"title":"Distracter-aware correlation filter tracking","authors":"Xiaohuan Lu, Zhenyu He","doi":"10.1109/SPAC.2017.8304244","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304244","url":null,"abstract":"We propose a distracter-aware Correlation Filters (CF) tracking algorithm, which exploits the information of dis-tracters to enhance the robustness of the tracker. Although most existing correlation filters based trackers achieve accurate tracking results, they may be less effective when similar distracters appear in the background. To this end, the proposed algorithm not only take the information of the target into consideration but also pay attention to the information of the distracters in the background. We first detect the distracters based on the response of CF model and then design a label map based on the information of the detected distracters. Unlike most existing CF based trackers which direct use a Gaussian shape label map, the proposed algorithm design a distracter-aware label map which makes the trained CF model effective to handle distracters. The proposed algorithm has several compelling advantages: it detects distracters, captures the discriminative information, which is crucial for robust tracking, enhances the robustness. We evaluate our proposed algorithm on the public OTB datasets, which including 50 sequences, and compare it with several state-of-the-art trackers. The comparable results show the effectiveness of the proposed 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":"127762750","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

Multilevel thresholding selection based on the fireworks algorithm for image segmentation 基于烟花算法的多级阈值选择图像分割

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304271

Hongwe Chen, Xingpeng Deng, Laiyi Yan, Z. Ye

引用次数: 3

Leucorrhea-wet-film recognition based on coarse-to-fine CNN-SVM 基于粗到精CNN-SVM的白带湿膜识别

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304338

Xiang Tian, Rui Guo, Qingbin Wu, Meiqin Wang, Yuxuan Su

引用次数: 0

Improved label noise identification by exploiting unlabeled data 利用未标记数据改进标签噪声识别

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304291

Hongqiang Wei, Qi Zhu, D. Guan, Weiwei Yuan, A. Khattak, Francis Chow

{"title":"Improved label noise identification by exploiting unlabeled data","authors":"Hongqiang Wei, Qi Zhu, D. Guan, Weiwei Yuan, A. Khattak, Francis Chow","doi":"10.1109/SPAC.2017.8304291","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304291","url":null,"abstract":"In machine learning, the available training samples are not always perfect and some labels can be corrupted which are called label noises. This may cause the reduction of accuracy. Meanwhile it will also increase the complexity of model. To mitigate the detrimental effect of label noises, noise filtering has been widely used which tries to identify label noises and remove them prior to learning. Almost all existing works only focus on the mislabeled training dataset and ignore the existence of unlabeled data. In fact, unlabeled data are easily accessible in many applications. In this work, we explore how to utilize these unlabeled data to increase the noise filtering effect. To this end, we have proposed a method named MFUDCM (Multiple Filtering with the aid of Unlabeled Data using Confidence Measurement). This method applies the novel multiple soft majority voting idea to make use unlabeled data. In addition, MFUDCM is expected to have a higher accuracy of identifying mislabeled data by using the concept of multiple voting. Finally, the validity of the proposed method MFUDCM is confirmed by experiments and the comparison results with other methods.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"29 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":"127168005","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

The extended collaborative representation-based classification 扩展的协作式基于表示的分类

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304260

Jianping Gou, Bing Hou, Weihua Ou, Jia Ke, Hebiao Yang, Yong Liu

{"title":"The extended collaborative representation-based classification","authors":"Jianping Gou, Bing Hou, Weihua Ou, Jia Ke, Hebiao Yang, Yong Liu","doi":"10.1109/SPAC.2017.8304260","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304260","url":null,"abstract":"Collaborative representation (CR), one of the well-known representation methods, has been widely used in pattern recognition. The collaborative representation-based classification (CRC) is to represent a test sample by the collaborative subspace of all the training samples from all classes. As an effective extension of CRC, the probabilistic collaborative representation-based classification (PCRC) calculates the probability of a test sample belonging to the collaborative subspace of all classes for classification. In the related CRC works, the representation fidelity is often measured by the ℓ2-norm of coding residual, but the ℓ1-norm fidelity is used very little. In fact, the representation fidelity with different coding residuals has a great effect on the CR-based classification performance. In this paper, to further improve the CR-based classification accuracy, we propose the extended CRC and PCRC by jointing the ℓ1-norm and ℓ2-norm of coding residuals on the representation fidelity. Besides, the extension of CRC is introduced by constraining the coding residual with ℓ1-norm. The experiments on four popular face databases show that the proposed extensions of CRC and PCRC perform very well.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"37 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":"123178163","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

SafeBox: A scheme for searching and sharing encrypted data in cloud applications SafeBox:一种在云应用程序中搜索和共享加密数据的方案

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304356

Guofeng Wang, Chuanyi Liu, Yingfei Dong, Hezhong Pan, Peiyi Han, Binxing Fang

引用次数: 6

Thermal infrared object tracking via Siamese convolutional neural networks 基于暹罗卷积神经网络的热红外目标跟踪

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304241

Qiao Liu, Di Yuan, Zhenyu He

引用次数: 6

The prediction on the election of representatives 对众议员选举的预测

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304299

Binyang Li, Dongdong Guo, M. Chang, Meng Li, Anny Bian

引用次数: 5

Height estimation of 3D surface based PMS in the natural environment 自然环境下基于PMS的三维表面高度估计

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304353

Ziqi Zhou, Zhenyu Guo, Xiaoting Sun

引用次数: 0