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2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)最新文献

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A Context-Aware Nonnegative Matrix Factorization Framework for Traffic Accident Risk Estimation via Heterogeneous Data 基于异构数据的交通事故风险估计环境感知非负矩阵分解框架
2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2018-04-10 DOI: 10.1109/MIPR.2018.00077
Quanjun Chen, Xuan Song, Z. Fan, Tianqi Xia, Harutoshi Yamada, R. Shibasaki
{"title":"A Context-Aware Nonnegative Matrix Factorization Framework for Traffic Accident Risk Estimation via Heterogeneous Data","authors":"Quanjun Chen, Xuan Song, Z. Fan, Tianqi Xia, Harutoshi Yamada, R. Shibasaki","doi":"10.1109/MIPR.2018.00077","DOIUrl":"https://doi.org/10.1109/MIPR.2018.00077","url":null,"abstract":"Traffic accidents have significantly globally increased over the past decades. The safety of transportation system has become an important issue for human society. Efficiently estimating accident risk will help for alleviating these safety issues and improving safety investment. As accidents are always caused by complex factors, heterogeneous data and a suitable model to combine these data information are needed in accident risk analysis. In this paper, we proposed a framework which utilizes matrix factorization method to estimate accident risk. First, we collect heterogeneous data and extract features from them so that we can get feature matrices to describe the background when accidents happened. Furthermore, we utilize context-aware non-negative matrix factorization method to model accident risk in a citywide scale. The results validate the efficiency of our model, and suggest that accident risk estimation can be significantly more accurate with heterogeneous data even accident data is missing or environment changes.","PeriodicalId":320000,"journal":{"name":"2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126792292","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
Understanding User Profiles on Social Media for Fake News Detection 了解用户在社交媒体上的配置文件，以检测假新闻
2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2018-04-10 DOI: 10.1109/MIPR.2018.00092
Kai Shu, Suhang Wang, Huan Liu
{"title":"Understanding User Profiles on Social Media for Fake News Detection","authors":"Kai Shu, Suhang Wang, Huan Liu","doi":"10.1109/MIPR.2018.00092","DOIUrl":"https://doi.org/10.1109/MIPR.2018.00092","url":null,"abstract":"Consuming news from social media is becoming increasingly popular nowadays. Social media brings benefits to users due to the inherent nature of fast dissemination, cheap cost, and easy access. However, the quality of news is considered lower than traditional news outlets, resulting in large amounts of fake news. Detecting fake news becomes very important and is attracting increasing attention due to the detrimental effects on individuals and the society. The performance of detecting fake news only from content is generally not satisfactory, and it is suggested to incorporate user social engagements as auxiliary information to improve fake news detection. Thus it necessitates an in-depth understanding of the correlation between user profiles on social media and fake news. In this paper, we construct real-world datasets measuring users trust level on fake news and select representative groups of both “experienced” users who are able to recognize fake news items as false and “naïve” users who are more likely to believe fake news. We perform a comparative analysis over explicit and implicit profile features between these user groups, which reveals their potential to differentiate fake news. The findings of this paper lay the foundation for future automatic fake news detection research.","PeriodicalId":320000,"journal":{"name":"2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115020653","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: 252
Automatic Feature Subset Selection for Clustering Images using Differential Evolution 基于差分进化的聚类图像特征子集自动选择
2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2018-04-10 DOI: 10.1109/MIPR.2018.00051
V. S. Srinivas, A. Srikrishna, B. E. Reddy
{"title":"Automatic Feature Subset Selection for Clustering Images using Differential Evolution","authors":"V. S. Srinivas, A. Srikrishna, B. E. Reddy","doi":"10.1109/MIPR.2018.00051","DOIUrl":"https://doi.org/10.1109/MIPR.2018.00051","url":null,"abstract":"Storing and organizing huge collection of image databases is a challenge for many applications. Such huge collection of images can be organized efficiently using image content clustering. Image Clustering is mapping of images into classes according to their similarity without any prior knowledge. Clustering of images into groups can improve the efficiency of searching images in the database for various web applications. Image content characterization greatly influences the result of clustering. This paper addresses the problem of characterizing and clustering a set of images using Differential Evolution. This work proposes a new algorithm, Automatic Feature Subset Selection for Clustering Images using Differential Evolution (AFSCIDE), to characterize the images with proper selection of textural features by feature subset selection and find groups with clustering using Differential Evolution. Experiments are conducted on various benchmark datasets CUReT, UIUC.","PeriodicalId":320000,"journal":{"name":"2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"851 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125419004","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
SecureCMerge: Secure PDF Merging over Untrusted Servers SecureCMerge:安全的PDF合并不受信任的服务器
2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2018-04-10 DOI: 10.1109/MIPR.2018.00087
N. Sharma, Priyanka Singh, P. Atrey
{"title":"SecureCMerge: Secure PDF Merging over Untrusted Servers","authors":"N. Sharma, Priyanka Singh, P. Atrey","doi":"10.1109/MIPR.2018.00087","DOIUrl":"https://doi.org/10.1109/MIPR.2018.00087","url":null,"abstract":"Merging two or more PDF files is an operation that is commonly performed by users, most often using freely available online tools, such as pdfmerge, or cloud-based services, such as cloudconvert. These free online servers cannot always be trusted and can often pose great risk to the confidentiality of the PDF files. In this paper, we present a method, called SecureCMerge, to merge the PDF files using free online merge sites in a secure way. Our core idea is to encrypt the content (text and image blocks) in the PDF files using Shamir's Secret Sharing (SSS) scheme before uploading it to a PDF merge server. We also show that the SSS scheme is merge homomorphic and the proposed method, by virtue of using the SSS scheme, provides information theoretic security. Experimental results demonstrate that the proposed method accomplishes secure PDF merging at an acceptable overhead in computation time and size.","PeriodicalId":320000,"journal":{"name":"2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125474622","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
Immersive, Social Applications for 8K Displays 8K显示器的沉浸式社交应用
2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2018-04-10 DOI: 10.1109/MIPR.2018.00031
V. Bove
{"title":"Immersive, Social Applications for 8K Displays","authors":"V. Bove","doi":"10.1109/MIPR.2018.00031","DOIUrl":"https://doi.org/10.1109/MIPR.2018.00031","url":null,"abstract":"Collaborations between the MIT Media Lab, NHK, and Toshiba Memory Corporation are exploring applications for large touch- and gesture-sensing 8K screens, including collaborative educational games, interactive visualizations for extremely dense anatomical datasets, and novel interfaces for exploring large media archives. In this paper I give an overview of the project, describe four representative applications, and provide design and user experience lessons learned.","PeriodicalId":320000,"journal":{"name":"2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125501271","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
Soccer Fans Sentiment through the Eye of Big Data: The UEFA Champions League as a Case Study 大数据视角下的球迷情绪:以欧洲冠军联赛为例
2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2018-04-10 DOI: 10.1109/MIPR.2018.00058
Samah Aloufi, Fatimah Al-Zamzami, M. Hoda, Abdulmotaleb El Saddik
{"title":"Soccer Fans Sentiment through the Eye of Big Data: The UEFA Champions League as a Case Study","authors":"Samah Aloufi, Fatimah Al-Zamzami, M. Hoda, Abdulmotaleb El Saddik","doi":"10.1109/MIPR.2018.00058","DOIUrl":"https://doi.org/10.1109/MIPR.2018.00058","url":null,"abstract":"The proliferation of online soccer contents generated by fans and teams has been widely leveraged in the area of sentiment analysis research. This work proposes domain-specific approach for understanding sentiment expressed in soccer conversations. We introduce a soccer-specific lexicon that we leverage in building sentiment model trained on soccer dataset. Our results show the effectiveness of the proposed approach in recognizing fans emotion during soccer events.","PeriodicalId":320000,"journal":{"name":"2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122524444","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: 11
Sequential Deep Learning for Disaster-Related Video Classification 灾难相关视频分类的顺序深度学习
2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2018-04-10 DOI: 10.1109/MIPR.2018.00026
Haiman Tian, Hector Cen Zheng, Shu‐Ching Chen
{"title":"Sequential Deep Learning for Disaster-Related Video Classification","authors":"Haiman Tian, Hector Cen Zheng, Shu‐Ching Chen","doi":"10.1109/MIPR.2018.00026","DOIUrl":"https://doi.org/10.1109/MIPR.2018.00026","url":null,"abstract":"Videos serve to convey complex semantic information and ease the understanding of new knowledge. However, when mixed semantic meanings from different modalities (i.e., image, video, text) are involved, it is more difficult for a computer model to detect and classify the concepts (such as flood, storm, and animals). This paper presents a multimodal deep learning framework to improve video concept classification by leveraging recent advances in transfer learning and sequential deep learning models. Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN) models are then used to obtain the sequential semantics for both audio and textual models. The proposed framework is applied to a disaster-related video dataset that includes not only disaster scenes, but also the activities that took place during the disaster event. The experimental results show the effectiveness of the proposed framework.","PeriodicalId":320000,"journal":{"name":"2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"351 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123331596","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: 12
Vehicle Detection in UAV Traffic Video Based on Convolution Neural Network 基于卷积神经网络的无人机交通视频车辆检测
2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2018-04-10 DOI: 10.1109/MIPR.2018.00009
Shulin Li, W. Zhang, Guorong Li, Li Su, Qingming Huang
{"title":"Vehicle Detection in UAV Traffic Video Based on Convolution Neural Network","authors":"Shulin Li, W. Zhang, Guorong Li, Li Su, Qingming Huang","doi":"10.1109/MIPR.2018.00009","DOIUrl":"https://doi.org/10.1109/MIPR.2018.00009","url":null,"abstract":"Vehicle detection technology is a key component of an intelligent transportation system, but most of the current vehicle detection technologies are based on road monitoring cameras. Compared with these fixed cameras, Unmanned Aerial Vehicles (UAVs) seem to have a lot of advantages such as more flexible, broader vision, higher speed, which make the vehicle detection more challenging. In this paper, a new dataset built on UAV traffic videos and a neural network which could fuse multi-layer features are proposed. Different from some networks with only a single layer, the proposed network merges the features from multiple layers firstly. Then a convolution layer is used to reduce the feature dimensions and a deconvolution layer is employed to do upsampling and enhance the response information. Finally, multiple fully connected layers are used to finish the detection. Furthermore, the proposed method combines the detecting and tracking for optimization and high detection speed. Experiments on the self-built UAV traffic video dataset demonstrate that the proposed method gets better results and higher speed.","PeriodicalId":320000,"journal":{"name":"2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134409209","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
Information Retrieval and Recommendation Using Emotion from Speech Signals 基于语音信号情感的信息检索与推荐
2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2018-04-10 DOI: 10.1109/MIPR.2018.00054
A. Iliev, P. Stanchev
{"title":"Information Retrieval and Recommendation Using Emotion from Speech Signals","authors":"A. Iliev, P. Stanchev","doi":"10.1109/MIPR.2018.00054","DOIUrl":"https://doi.org/10.1109/MIPR.2018.00054","url":null,"abstract":"In this paper we describe a system of retrieving information from artwork based on textual cues, descriptive to relative art pieces, made available through the metadata itself. Large datasets of artwork can easily be mined by using alternative queries and search methodologies. In the most common search methodology a text-based query using a keyboard is performed. We are proposing a method for searching, finding and recommending digital media content based on pre-set metadata text queries organized in two categories, then mapped to speech sentiment cues extracted from the emotion layer of speech alone. We also account for the difference in sentiment expression for male and female speakers and further suggest that this differentiation may improve system performance.","PeriodicalId":320000,"journal":{"name":"2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133758177","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
Quantitative Analysis of Renal Fibrosis Using a Colorimetric System 使用比色系统定量分析肾纤维化
2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) Pub Date : 2018-04-10 DOI: 10.1109/MIPR.2018.00040
Cassandra L. Reed, Shree G. Sharma, Sandra P. Prieto, Timothy J. Muldoon
{"title":"Quantitative Analysis of Renal Fibrosis Using a Colorimetric System","authors":"Cassandra L. Reed, Shree G. Sharma, Sandra P. Prieto, Timothy J. Muldoon","doi":"10.1109/MIPR.2018.00040","DOIUrl":"https://doi.org/10.1109/MIPR.2018.00040","url":null,"abstract":"Manual assessment of renal fibrosis via visual inspection represents a significant challenge even among expert pathologists. The goal of this project was to create a quantitative analysis program that could be used as a complementary tool for pathologists. The algorithm was applied to color images of renal needle core biopsy slides, at varying stages of disease, stained with either Masson's trichrome (MT) or collagen III immunohistochemistry (Coll3). Each image was split into colorimetric channels and the ratio of two of the corresponding channels was then compared to the overall tissue image. Results demonstrated that colorimetric ratios of red:blue for MT and green:red for Coll3 produced ranges between 1.3- 1.5 and 1.10-1.12, respectively, that successfully excluded non-fibrotic renal tissue and could therefore be used to create a quantitative analysis of renal fibrosis for comparison to pathologists' diagnosis.","PeriodicalId":320000,"journal":{"name":"2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131387496","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
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