Proceedings of the 21st ACM international conference on Multimedia最新文献_第5页

Online multimodal deep similarity learning with application to image retrieval 在线多模态深度相似学习及其在图像检索中的应用

Proceedings of the 21st ACM international conference on Multimedia Pub Date : 2013-10-21 DOI: 10.1145/2502081.2502112

Pengcheng Wu, S. Hoi, Hao Xia, P. Zhao, Dayong Wang, C. Miao

{"title":"Online multimodal deep similarity learning with application to image retrieval","authors":"Pengcheng Wu, S. Hoi, Hao Xia, P. Zhao, Dayong Wang, C. Miao","doi":"10.1145/2502081.2502112","DOIUrl":"https://doi.org/10.1145/2502081.2502112","url":null,"abstract":"Recent years have witnessed extensive studies on distance metric learning (DML) for improving similarity search in multimedia information retrieval tasks. Despite their successes, most existing DML methods suffer from two critical limitations: (i) they typically attempt to learn a linear distance function on the input feature space, in which the assumption of linearity limits their capacity of measuring the similarity on complex patterns in real-world applications; (ii) they are often designed for learning distance metrics on uni-modal data, which may not effectively handle the similarity measures for multimedia objects with multimodal representations. To address these limitations, in this paper, we propose a novel framework of online multimodal deep similarity learning (OMDSL), which aims to optimally integrate multiple deep neural networks pretrained with stacked denoising autoencoder. In particular, the proposed framework explores a unified two-stage online learning scheme that consists of (i) learning a flexible nonlinear transformation function for each individual modality, and (ii) learning to find the optimal combination of multiple diverse modalities simultaneously in a coherent process. We conduct an extensive set of experiments to evaluate the performance of the proposed algorithms for multimodal image retrieval tasks, in which the encouraging results validate the effectiveness of the proposed technique.","PeriodicalId":20448,"journal":{"name":"Proceedings of the 21st ACM international conference on Multimedia","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86132257","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: 169

Stereotime: a wireless 2D and 3D switchable video communication system 立体时间:一个无线2D和3D切换视频通信系统

Proceedings of the 21st ACM international conference on Multimedia Pub Date : 2013-10-21 DOI: 10.1145/2502081.2502275

You Yang, Qiong Liu, Yue Gao, Binbin Xiong, Li Yu, Huanbo Luan, R. Ji, Q. Tian

引用次数: 5

Jiku director: a mobile video mashup system Jiku导演:一个移动视频混搭系统

Proceedings of the 21st ACM international conference on Multimedia Pub Date : 2013-10-21 DOI: 10.1145/2502081.2502277

Duong-Trung-Dung Nguyen, M. Saini, Vu-Thanh Nguyen, Wei Tsang Ooi

引用次数: 8

Action recognition using invariant features under unexampled viewing conditions 在未示例的观看条件下使用不变特征的动作识别

Proceedings of the 21st ACM international conference on Multimedia Pub Date : 2013-10-21 DOI: 10.1145/2502081.2508126

Litian Sun, K. Aizawa

引用次数: 14

SentiBank: large-scale ontology and classifiers for detecting sentiment and emotions in visual content SentiBank:用于检测视觉内容中的情绪和情绪的大规模本体和分类器

Proceedings of the 21st ACM international conference on Multimedia Pub Date : 2013-10-21 DOI: 10.1145/2502081.2502268

Damian Borth, Tao Chen, R. Ji, Shih-Fu Chang

引用次数: 190

Multimedia information retrieval: music and audio 多媒体信息检索:音乐和音频

Proceedings of the 21st ACM international conference on Multimedia Pub Date : 2013-10-21 DOI: 10.1145/2502081.2502237

M. Schedl, E. Gómez, Masataka Goto

{"title":"Multimedia information retrieval: music and audio","authors":"M. Schedl, E. Gómez, Masataka Goto","doi":"10.1145/2502081.2502237","DOIUrl":"https://doi.org/10.1145/2502081.2502237","url":null,"abstract":"Music is an omnipresent topic in our daily lives, as almost everyone enjoys listening to his or her favorite tunes. Music information retrieval (MIR) is a research field that aims – among other things – at automatically extracting semantically meaningful information from various representations of music entities, such as a digital audio file, a band’s web page, a song’s lyrics, or a tweet about a microblogger’s current listening activity. A key approach in MIR is to describe music via computational features, which can be categorized into: music content, music context, and user context. The music content refers to features extracted from the audio signal, while information about musical entities not encoded in the signal (e.g., image of an artist or political background of a song) are referred to as music context. The user context, in contrast, includes environmental aspects as well as physical and mental activities of the music listener. MIR research has been seeing a paradigm shift over the last couple of years, as an increasing number of recent approaches and commercial technologies combine content-based techniques (focusing on the audio signal) with multimedia context data mined, e.g. from web sources and with user context information.","PeriodicalId":20448,"journal":{"name":"Proceedings of the 21st ACM international conference on Multimedia","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85130970","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

Moment feature based forensic detection of resampled digital images 基于矩特征的重采样数字图像法医检测

Proceedings of the 21st ACM international conference on Multimedia Pub Date : 2013-10-21 DOI: 10.1145/2502081.2502150

Lu Li, Jianru Xue, Zhiqiang Tian, Nanning Zheng

引用次数: 17

Session details: Scene understanding 会话细节:场景理解

Proceedings of the 21st ACM international conference on Multimedia Pub Date : 2013-10-21 DOI: 10.1145/3245301

D. Joshi

引用次数: 0

Session details: Security and forensics 会话详细信息:安全性和取证

Proceedings of the 21st ACM international conference on Multimedia Pub Date : 2013-10-21 DOI: 10.1145/3245296

R. Cucchiara

引用次数: 0

Golden retriever: a Java based open source image retrieval engine 一个基于Java的开源图像检索引擎

Proceedings of the 21st ACM international conference on Multimedia Pub Date : 2013-10-21 DOI: 10.1145/2502081.2502227

Lazaros Tsochatzidis, C. Iakovidou, S. Chatzichristofis, Y. Boutalis

引用次数: 4