2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)最新文献_第2页

Visual object tracking based on particle filter re-detection 基于粒子滤波再检测的视觉目标跟踪

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304242

Di Yuan, Guanglei Zhao, Donghao Li, Zhenyu He, Nan Luo

引用次数: 3

Attribute-based queries over outsourced encrypted database 外包加密数据库上基于属性的查询

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304269

Z. L. Jiang, Jiajun Huang, Zechao Liu, Xuan Wang

引用次数: 0

Spatial-scale-based blur kernel estimation for blind motion deblurring 基于空间尺度的运动去模糊模糊核估计

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304286

Shu Tang, Xianzhong Xie, Xiao Luan, M. Xia, Peisong Liu

引用次数: 1

Generative caption for diabetic retinopathy images 糖尿病视网膜病变图像的生成说明

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304332

Luhui Wu, Cheng Wan, Yiquan Wu, Jiang Liu

{"title":"Generative caption for diabetic retinopathy images","authors":"Luhui Wu, Cheng Wan, Yiquan Wu, Jiang Liu","doi":"10.1109/SPAC.2017.8304332","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304332","url":null,"abstract":"For a long time, the detection of diabetic retinopathy has always been a great challenge. People want to find a fast and effective computer-aided treatment to diagnose the disease. In recent years, the rapid development of the deep learning makes it gradually become an effective technique for the analysis of medical images. In this paper, we propose a method to deal with diabetic retinopathy images with generative caption technique of images to generate a simple sequence to explain the abnormal contents in fundus images. The generative technique of images is a generative model based on a deep recurrent architecture that combines convolution neural network (CNN) which is currently state-of-the-art for object recognition and detection with long-short-term-memory (LSTM) which is applied with great success to machine translation and sequence generation, and that can be used to generate natural sentences describing an image. The target of the model in training is to maximize the likelihood of the target description sentence given from the training images. The model built on dataset DIARETDB0, DIARETDB1 and Messidor can achieve good performance and generate fluent sequences. In addition, the experimental results show that the accuracy of diagnosis for individual abnormal discoveries is up to 88.53% and the diagnosis accuracy is more than 90%.","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":"132808184","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

Sparse selective kernelized correlation filter model for visual object tracking 视觉目标跟踪的稀疏选择核化相关滤波模型

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304258

Xiaohuan Lu, Di Yuan, Zhenyu He, Donghao Li

引用次数: 2

A face recognition method based on residual image representation and feature extraction 基于残差图像表示和特征提取的人脸识别方法

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304354

Linghui Liu, Xiao Luan, Shu Tang, Hongmin Geng, Ye Zhang

{"title":"A face recognition method based on residual image representation and feature extraction","authors":"Linghui Liu, Xiao Luan, Shu Tang, Hongmin Geng, Ye Zhang","doi":"10.1109/SPAC.2017.8304354","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304354","url":null,"abstract":"To address the problem of non-well controlled face recognition, such as illumination changes, pose variation and random pixel corruption, we propose a robust face recognition method based on representation and feature extraction of residual images. Represented by sparse representation and linear regression, linear representation methods typically use training samples to represent and reconstruct test samples, and determine classification results according to the distance between test samples and reconstruction samples. In this paper, we consider to use linear regression to obtain reconstruction samples of the test sample with respect to each subject, and compute residual images by the difference between test sample and reconstruction samples. Then we analyze intensity distribution of residual images between the correct subject and other subjects, and adopt intensity transform to surpass the intra-class difference and strengthen the inter-class difference. Finally, we use wavelet decomposition to extract global intensity distribution of residual images, and introduce information entropy to illustrate the uncertainty of intensity distribution, which are extracted as discriminating features. Compared with several popular face recognition methods, the efficacy of the proposed method is verified on four popular face databases (i.e., ORL, Extended Yale B, Georgia Tech and AR) with promising results.","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":"133440708","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

Chinese word semantic relation classification based on multiple knowledge resources 基于多知识资源的汉语词语义关系分类

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304307

Fanqing Meng, Yuteng Zhang, Wenpeng Lu, Weiyu Zhang, Jinyong Cheng

引用次数: 0

Online discriminant projective non-negative matrix factorization 判别投影非负矩阵在线分解

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304336

Xiang Zhang, Qing Liao, Zhigang Luo

{"title":"Online discriminant projective non-negative matrix factorization","authors":"Xiang Zhang, Qing Liao, Zhigang Luo","doi":"10.1109/SPAC.2017.8304336","DOIUrl":"https://doi.org/10.1109/SPAC.2017.8304336","url":null,"abstract":"Projective non-negative matrix factorization (PNMF) learns a subspace spanned by several non-negative bases by minimizing the distance between samples and their reconstructions in the subspace. Due to its effective representation ability, PNMF has attracted a lot of attention in the computer vision community. However, PNMF suffers from the following limitations: 1) it requires entire dataset to reside in computer's memory, and as a consequence it cannot handle large-scale or streaming data, and 2) it completely ignores discriminative information of available labeled data, and thus has poor performance in classification tasks. Here, we propose an online discriminant PNMF (ODPNMF) method to overcome these deficiencies. Specifically, ODPNMF receives one or a few samples per step and updates the basis via a multiplicative update rule (MUR), which guarantees the non-negativity constraint over basis. To best utilize discriminative information, ODPNMF maintains and adaptively updates both within-class and between-class scatter matrices, during each round of updating the basis. Experimental results on three popular face image datasets verify the effectiveness of ODPNMF compared to representative algorithms.","PeriodicalId":161647,"journal":{"name":"2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)","volume":"30 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":"122081732","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 convolutional neural networks for emotion recognition based on EEG signal 基于脑电信号的卷积神经网络情感识别

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304360

Zhiyuan Wen, Ruifeng Xu, Jiachen Du

引用次数: 54

A real-time moving target tracking algorithm based on SIFT 基于SIFT的实时运动目标跟踪算法

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) Pub Date : 2017-12-01 DOI: 10.1109/SPAC.2017.8304342

H. Qiang, C. Qian, Baojiang Zhong

引用次数: 0