2017 IEEE International Conference on Multimedia and Expo (ICME)最新文献_第4页

How competitive are you: Analysis of people's attractiveness in an online dating system 你的竞争力如何:在线约会系统中人们的吸引力分析

2017 IEEE International Conference on Multimedia and Expo (ICME) Pub Date : 2017-07-10 DOI: 10.1109/ICME.2017.8019374

Xiaoxue Zang, T. Yamasaki, K. Aizawa, Tetsuhiro Nakamoto, E. Kuwabara, Shinichi Egami, Yusuke Fuchida

引用次数: 2

Notify-and-interact: A beacon-smartphone interaction for user engagement in galleries 通知-交互:信标-智能手机交互，用于用户参与画廊

2017 IEEE International Conference on Multimedia and Expo (ICME) Pub Date : 2017-07-10 DOI: 10.1109/ICME.2017.8019467

Pai Chet Ng, James She, Soochang Park

引用次数: 24

An extended probabilistic collaborative representation based classifier for image classification 基于扩展概率协同表示的图像分类器

2017 IEEE International Conference on Multimedia and Expo (ICME) Pub Date : 2017-07-10 DOI: 10.1109/ICME.2017.8019308

Rushi Lan, Yicong Zhou

引用次数: 20

Cube surface modeling for human detection in crowd 人群中人体检测的立方体曲面建模

2017 IEEE International Conference on Multimedia and Expo (ICME) Pub Date : 2017-07-10 DOI: 10.1109/ICME.2017.8019311

Jing Li, Fangbing Zhang, Lisong Wei, Tao Yang, Zhongzhen Li

引用次数: 2

Image captioning with deep LSTM based on sequential residual 基于序列残差的深度LSTM图像字幕

2017 IEEE International Conference on Multimedia and Expo (ICME) Pub Date : 2017-07-10 DOI: 10.1109/ICME.2017.8019408

Kaisheng Xu, Hanli Wang, Pengjie Tang

引用次数: 22

Learning-based adaptive tone mapping for keypoint detection 基于学习的自适应音调映射关键点检测

2017 IEEE International Conference on Multimedia and Expo (ICME) Pub Date : 2017-07-10 DOI: 10.1109/ICME.2017.8019394

A. Rana, G. Valenzise, F. Dufaux

引用次数: 10

Single depth image super-resolution with multiple residual dictionary learning and refinement 单深度图像超分辨率与多个残差字典学习和细化

2017 IEEE International Conference on Multimedia and Expo (ICME) Pub Date : 2017-07-10 DOI: 10.1109/ICME.2017.8019331

Lijun Zhao, H. Bai, Jie Liang, Anhong Wang, Yao Zhao

引用次数: 11

Estimating political leanings from mass media via graph-signal restoration with negative edges 通过带负边的图形信号恢复估计大众媒体的政治倾向

2017 IEEE International Conference on Multimedia and Expo (ICME) Pub Date : 2017-07-10 DOI: 10.1109/ICME.2017.8019302

B. Renoust, Gene Cheung, S. Satoh

{"title":"Estimating political leanings from mass media via graph-signal restoration with negative edges","authors":"B. Renoust, Gene Cheung, S. Satoh","doi":"10.1109/ICME.2017.8019302","DOIUrl":"https://doi.org/10.1109/ICME.2017.8019302","url":null,"abstract":"Politicians in the same political party often share the same views on social issues and legislative agendas. By mining patterns in TV news co-appearances and Twitter followers, in this paper we estimate political leanings (left / right) of unknown individuals, and detect outlier politicians who have views different from their colleagues in the same party, from a graph signal processing (GSP) perspective. Specifically, we first construct a similarity graph with politicians as nodes, where a positive edge connects two politicians with sizable shared Twitter followers, and a negative edge connects two politicians appearing in the same TV news segment (and thus likely take opposite stands on the same issue). Given a graph with both positive and negative edges, we propose a new graph-signal smoothness prior based on a constructed generalized graph Laplacian matrix that is guaranteed to be positive semi-definite. We formulate a graph-signal restoration problem that can be solved in closed form. Experimental results show that political leanings of unknown individuals can be reliably estimated and outlier politicians can be detected.","PeriodicalId":330977,"journal":{"name":"2017 IEEE International Conference on Multimedia and Expo (ICME)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121793891","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: 6

Geographic information use in weakly-supervised deep learning for landmark recognition 地理信息在弱监督深度学习中用于地标识别

2017 IEEE International Conference on Multimedia and Expo (ICME) Pub Date : 2017-07-10 DOI: 10.1109/ICME.2017.8019376

Yifang Yin, Zhenguang Liu, Roger Zimmermann

{"title":"Geographic information use in weakly-supervised deep learning for landmark recognition","authors":"Yifang Yin, Zhenguang Liu, Roger Zimmermann","doi":"10.1109/ICME.2017.8019376","DOIUrl":"https://doi.org/10.1109/ICME.2017.8019376","url":null,"abstract":"The successful deep convolutional neural networks for visual object recognition typically rely on a massive number of training images that are well annotated by class labels or object bounding boxes with great human efforts. Here we explore the use of the geographic metadata, which are automatically retrieved from sensors such as GPS and compass, in weakly-supervised learning techniques for landmark recognition. The visibility of a landmark in a frame can be calculated based on the camera's field-of-view and the landmark's geometric information such as location and height. Subsequently, a training dataset is generated as the union of the frames with presence of at least one target landmark. To reduce the impact of the intrinsic noise in the geo-metadata, we present a frame selection method that removes the mistakenly labeled frames with a two-step approach consisting of (1) Gaussian Mixture Model clustering based on camera location followed by (2) outlier removal based on visual consistency. We compare the classification results obtained from the ground truth labels and the noisy labels derived from the raw geo-metadata. Experiments show that training based on the raw geo-metadata achieves a Mean Average Precision (MAP) of 0.797. Moreover, by applying our proposed representative frame selection method, the MAP can be further improved by 6.4%, which indicates the promising use of the geo-metadata in weakly-supervised learning techniques.","PeriodicalId":330977,"journal":{"name":"2017 IEEE International Conference on Multimedia and Expo (ICME)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115817680","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: 7

Dynamic backlight scaling considering ambient luminance for mobile energy saving 考虑环境亮度的动态背光缩放移动节能

2017 IEEE International Conference on Multimedia and Expo (ICME) Pub Date : 2017-07-10 DOI: 10.1109/ICME.2017.8019511

Wei Sun, Guangtao Zhai, Xiongkuo Min, Yutao Liu, Siwei Ma, Jing Liu, Jiantao Zhou, Xianming Liu

{"title":"Dynamic backlight scaling considering ambient luminance for mobile energy saving","authors":"Wei Sun, Guangtao Zhai, Xiongkuo Min, Yutao Liu, Siwei Ma, Jing Liu, Jiantao Zhou, Xianming Liu","doi":"10.1109/ICME.2017.8019511","DOIUrl":"https://doi.org/10.1109/ICME.2017.8019511","url":null,"abstract":"The mobile video playback involves many subsystems of the devices such as computing, rendering and displaying subsystems. Among all subsystems, the displaying subsystem accounts for at least 38% of all consumed power, and it can be up to 68% with the maximum backlight brightness. What is more, lots of people watch videos via mobile devices in various situations, where the ambient luminance condition is different. Therefore, how to save mobile energy and improve the Quality of Experience (QoE) in different situations become significant problems. In this paper, we try to maximally enhance the battery power performance under various ambient luminance conditions through backlight magnitude adjusting, while without negatively impacting users' QoE. In particular, we conduct a series of subject quality assessment experiments to uncover the quantitative relationship among QoE, ambient luminance, video content luminance and backlight level. We first study whether the continuous playback of backlight-scaled shots using the proposed scaling magnitude would cause flicker effect or not. Then motivated by the findings of these subject studies, we implement a Dynamic Backlight Scaling (DBS) strategy. The experiment results demonstrate that the DBS strategy can save more than 40% power at most and can also save 10% power even at a very high ambient luminance.","PeriodicalId":330977,"journal":{"name":"2017 IEEE International Conference on Multimedia and Expo (ICME)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131847143","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