Proceedings of the 21st ACM international conference on Multimedia最新文献_第10页

Error recovered hierarchical classification 错误恢复层次分类

Proceedings of the 21st ACM international conference on Multimedia Pub Date : 2013-10-21 DOI: 10.1145/2502081.2502182

Shiai Zhu, Xiao-Yong Wei, C. Ngo

引用次数: 7

Activity-aware adaptive compression: a morphing-based frame synthesis application in 3DTI 活动感知自适应压缩:3DTI中基于变形的帧合成应用

Proceedings of the 21st ACM international conference on Multimedia Pub Date : 2013-10-21 DOI: 10.1145/2502081.2508116

Chien-Nan Chen, Pengye Xia, K. Nahrstedt

引用次数: 11

Cross-media topic mining on wikipedia 维基百科上的跨媒体主题挖掘

Proceedings of the 21st ACM international conference on Multimedia Pub Date : 2013-10-21 DOI: 10.1145/2502081.2502180

Xikui Wang, Yang Liu, Donghui Wang, Fei Wu

引用次数: 8

Speaking swiss: languages and venues in foursquare 说瑞士语:在四方的语言和场所

Proceedings of the 21st ACM international conference on Multimedia Pub Date : 2013-10-21 DOI: 10.1145/2502081.2502133

Darshan Santani, D. Gática-Pérez

引用次数: 10

A multigrid approach for bandwidth and display resolution aware streaming of 3D deformations 一种带宽和显示分辨率感知的3D变形流的多网格方法

Proceedings of the 21st ACM international conference on Multimedia Pub Date : 2013-10-21 DOI: 10.1145/2502081.2502181

Yuan Tian, Y. Yang, X. Guo, B. Prabhakaran

引用次数: 1

Session details: Similarity search 会话详细信息:相似性搜索

Proceedings of the 21st ACM international conference on Multimedia Pub Date : 2013-10-21 DOI: 10.1145/3245288

Yong Rui

引用次数: 0

Spot the differences: from a photograph burst to the single best picture 找出区别:从一张突发的照片到一张最好的照片

Proceedings of the 21st ACM international conference on Multimedia Pub Date : 2013-10-21 DOI: 10.1145/2502081.2502190

H. E. Tasli, J. V. Gemert, T. Gevers

引用次数: 1

Towards efficient sparse coding for scalable image annotation 面向可扩展图像标注的高效稀疏编码

Proceedings of the 21st ACM international conference on Multimedia Pub Date : 2013-10-21 DOI: 10.1145/2502081.2502127

Junshi Huang, Hairong Liu, Jialie Shen, Shuicheng Yan

{"title":"Towards efficient sparse coding for scalable image annotation","authors":"Junshi Huang, Hairong Liu, Jialie Shen, Shuicheng Yan","doi":"10.1145/2502081.2502127","DOIUrl":"https://doi.org/10.1145/2502081.2502127","url":null,"abstract":"Nowadays, content-based retrieval methods are still the development trend of the traditional retrieval systems. Image labels, as one of the most popular approaches for the semantic representation of images, can fully capture the representative information of images. To achieve the high performance of retrieval systems, the precise annotation for images becomes inevitable. However, as the massive number of images in the Internet, one cannot annotate all the images without a scalable and flexible (i.e., training-free) annotation method. In this paper, we particularly investigate the problem of accelerating sparse coding based scalable image annotation, whose off-the-shelf solvers are generally inefficient on large-scale dataset. By leveraging the prior that most reconstruction coefficients should be zero, we develop a general and efficient framework to derive an accurate solution to the large-scale sparse coding problem through solving a series of much smaller-scale subproblems. In this framework, an active variable set, which expands and shrinks iteratively, is maintained, with each snapshot of the active variable set corresponding to a subproblem. Meanwhile, the convergence of our proposed framework to global optimum is theoretically provable. To further accelerate the proposed framework, a sub-linear time complexity hashing strategy, e.g. Locality-Sensitive Hashing, is seamlessly integrated into our framework. Extensive empirical experiments on NUS-WIDE and IMAGENET datasets demonstrate that the orders-of-magnitude acceleration is achieved by the proposed framework for large-scale image annotation, along with zero/negligible accuracy loss for the cases without/with hashing speed-up, compared to the expensive off-the-shelf solvers.","PeriodicalId":20448,"journal":{"name":"Proceedings of the 21st ACM international conference on Multimedia","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89860240","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: 18

Motion matters: a novel framework for compressing surveillance videos 运动很重要:压缩监控视频的新框架

Proceedings of the 21st ACM international conference on Multimedia Pub Date : 2013-10-21 DOI: 10.1145/2502081.2502145

Xiaojie Guo, Siyuan Li, Xiaochun Cao

{"title":"Motion matters: a novel framework for compressing surveillance videos","authors":"Xiaojie Guo, Siyuan Li, Xiaochun Cao","doi":"10.1145/2502081.2502145","DOIUrl":"https://doi.org/10.1145/2502081.2502145","url":null,"abstract":"Currently, video surveillance plays a very important role in the fields of public safety and security. For storing the videos that usually contain extremely long sequences, it requires huge space. Video compression techniques can be used to release the storage load to some extent, such as H.264/AVC. However, the existing codecs are not sufficiently effective and efficient for encoding surveillance videos as they do not specifically consider the characteristic of surveillance videos, i.e. the background of surveillance video has intensive redundancy. This paper introduces a novel framework for compressing such videos. We first train a background dictionary based on a small number of observed frames. With the trained background dictionary, we then separate every frame into the background and motion (foreground), and store the compressed motion together with the reconstruction coefficient of the background corresponding to the background dictionary. The decoding is carried out on the encoded frame in an inverse procedure. The experimental results on extensive surveillance videos demonstrate that our proposed method significantly reduces the size of videos while gains much higher PSNR compared to the state of the art codecs.","PeriodicalId":20448,"journal":{"name":"Proceedings of the 21st ACM international conference on Multimedia","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89139035","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: 13

Social life networks: a multimedia problem? 社交生活网络:多媒体问题?

Proceedings of the 21st ACM international conference on Multimedia Pub Date : 2013-10-21 DOI: 10.1145/2502081.2502279

Amarnath Gupta, R. Jain

引用次数: 11