2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS)最新文献_第3页

Development of a parser for the AADL error model annex AADL错误模型附件解析器的开发

2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) Pub Date : 2017-05-24 DOI: 10.1109/ICIS.2017.7959999

Wafa Gabsi, Bechir Zalila, M. Jmaiel

引用次数: 0

3D model dynamic cutting technology based on game engine 基于游戏引擎的三维模型动态切割技术

2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) Pub Date : 2017-05-24 DOI: 10.1109/ICIS.2017.7960058

Wenfeng Hu, Shuang Zhao, Yu Ren

引用次数: 4

Semi-supervised distance metric learning for person re-identification 人再识别的半监督距离度量学习

2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) Pub Date : 2017-05-24 DOI: 10.1109/ICIS.2017.7960090

F. Chen, Jinhong Chai, Dinghu Ren, Xiaofang Liu, Yun Yang

{"title":"Semi-supervised distance metric learning for person re-identification","authors":"F. Chen, Jinhong Chai, Dinghu Ren, Xiaofang Liu, Yun Yang","doi":"10.1109/ICIS.2017.7960090","DOIUrl":"https://doi.org/10.1109/ICIS.2017.7960090","url":null,"abstract":"As a fundamental task in automated video surveillance, person re-identification, which has received increasing attention in recent years, aims to match people across non-overlapping camera views in a multi-camera surveillance system. It has been reported that KISS metric learning has been followed by most of the previous supervised work because of its state of the art performance for person re-identification on VIPeR dataset. However, given only a small number of labeled image pairs available for training, the matching model certainly suffers from unstable learning process and poor matching result. To address this serious practical issue, we proposed a novel semi-supervised KISS metric learning (SS-KISS) approach which makes use of unlabeled data to improve the re-identification performance by 1) combining both global and local information to select the most confident image pairs from the unlabeled data; 2) using an ensemble approach, which explores advantages of supervised and unsupervised learning by reconciling two matching models on which labeled and un-labeled data to an optimal one via smart weighting schema. Extensive experiments have been conducted on three datasets: VIPeR, ETHZ, and i-LiDS, experimental results demonstrate that our approach achieves a sound performance in the case of small amount of labeled data.","PeriodicalId":301467,"journal":{"name":"2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117179486","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: 5

Pan-and-tilt self-portrait system using gesture interface 平移和倾斜自画像系统使用手势界面

2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) Pub Date : 2017-05-24 DOI: 10.1109/ICIS.2017.7960063

Shaowei Chu, Fan Zhang, Naye Ji, Zhefan Jin, Ruifang Pan

{"title":"Pan-and-tilt self-portrait system using gesture interface","authors":"Shaowei Chu, Fan Zhang, Naye Ji, Zhefan Jin, Ruifang Pan","doi":"10.1109/ICIS.2017.7960063","DOIUrl":"https://doi.org/10.1109/ICIS.2017.7960063","url":null,"abstract":"Digital cameras are widely used in desktop and notebook PCs. Taking self-portraits is one of the important function of such cameras, which allows users to capture memories, create art, and improve photography techniques. A desktop environment with a large display and a pan-and-tilt camera provides users with a good area for exploring more angles and postures while taking self-portraits. However, most of the existing camera interfaces of this type are limited to device-based systems (i.e., mouse and keyboard) that prevent users from efficiently controlling the camera while taking self-portraits. This study proposes a vision-based system equipped with a gesture interface that control a pan-and-tilt camera for taking self-portraits. This interface uses gestures, particularly slight hand movements (i.e., sweeps, circles, and waves), to control the pan, tilt, and shutter functions of the camera. The gesture-recognition achieved good efficiency in performance (less than 2ms) and the recognition rate (0.9 on average in lighting conditions range 100–200). Experimental results indicate that the proposed system effectively controls the options in a self-portrait camera, this approach provides significantly higher satisfaction, particularly in terms of the intuitive motion gestures, freedom, and enjoyment, than when using a hand-held remote control or a conventional mouse-based interface. The proposed system is a promising technique for taking self-portraits in a desktop environment.","PeriodicalId":301467,"journal":{"name":"2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121293411","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

Naive Bayes classifiers for music emotion classification based on lyrics 基于歌词的朴素贝叶斯音乐情感分类器

2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) Pub Date : 2017-05-24 DOI: 10.1109/ICIS.2017.7960070

Yunjing An, Shutao Sun, Shujuan Wang

引用次数: 70

Research on RBF neural network in simulation of MBR membrane pollution simulation RBF神经网络在MBR膜污染模拟中的应用研究

2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) Pub Date : 2017-05-24 DOI: 10.1109/ICIS.2017.7960023

Xiangning Chen, Chunqing Li, Wenbo Hu, Jia Tang

引用次数: 1

Analysis of quality evaluation based on ISO/IEC SQuaRE series standards and its considerations 基于ISO/IEC SQuaRE系列标准的质量评价分析及其考虑

2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) Pub Date : 2017-05-24 DOI: 10.1109/ICIS.2017.7960001

Yi Zhao, Jiayu Gong, Yun Hu, Zhenyu Liu, Lizhi Cai

引用次数: 4

Fusion of LBP and HOG using multiple kernel learning for infrared face recognition 基于多核学习的LBP和HOG融合红外人脸识别

2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) Pub Date : 2017-05-24 DOI: 10.1109/ICIS.2017.7959973

Zhihua Xie, Peng-Chao Jiang, Shuai Zhang

引用次数: 22

Online news recommender based on stacked auto-encoder 基于堆叠自编码器的在线新闻推荐

2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) Pub Date : 2017-05-24 DOI: 10.1109/ICIS.2017.7960088

Sanxing Cao, Nan Yang, Zhengzheng Liu

引用次数: 33

K-means based on active learning for support vector machine 基于主动学习的K-means支持向量机

2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) Pub Date : 2017-05-24 DOI: 10.1109/ICIS.2017.7960089

J. Gan, Ang Li, Qian-Lin Lei, Hao Ren, Yun Yang

{"title":"K-means based on active learning for support vector machine","authors":"J. Gan, Ang Li, Qian-Lin Lei, Hao Ren, Yun Yang","doi":"10.1109/ICIS.2017.7960089","DOIUrl":"https://doi.org/10.1109/ICIS.2017.7960089","url":null,"abstract":"In practice, unlabeled data can be cheaply and easily collected from target domain, but it is quite difficult and expensive to obtain a large amount of labeled data. Therefore how to use both of labeled and unlabeled data to improve the learning performance becomes critical issue for many real-world applications. Active Learning and Semi-supervised Learning are right solutions to such problem, and have been intensively studied from different perspectives. The former one advocates that learner is able to control the entire dataset and actively query the labels from the target dataset, the latter one tries to improve the learner's performance by using both of labeled and unlabeled instances at the same time. In this paper, we propose an Active Learning based SVM approach, KA-SvM. According to a cluster hypothesis, we use k-means to construct a pre-selection scheme, which obtains a subset of important instances as training set, then SVM can be optimally trained on such subset rather than entire one. Our approach has been generally evaluated on several benchmark datasets with comparison with other similar approaches, the experiment results demonstrate that our approach has the outstanding performance on both of classification accuracy and computation efficiency.","PeriodicalId":301467,"journal":{"name":"2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS)","volume":"300 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122319826","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: 10