Proceedings of the 21st ACM international conference on Multimedia最新文献_第6页

Classifying tag relevance with relevant positive and negative examples 用相关的正反例对标签相关性进行分类

Proceedings of the 21st ACM international conference on Multimedia Pub Date : 2013-10-21 DOI: 10.1145/2502081.2502129

Xirong Li, Cees G. M. Snoek

引用次数: 40

Lecture video segmentation by automatically analyzing the synchronized slides 讲座视频分割，自动分析同步幻灯片

Proceedings of the 21st ACM international conference on Multimedia Pub Date : 2013-10-21 DOI: 10.1145/2502081.2508115

Xiaoyin Che, Haojin Yang, C. Meinel

引用次数: 42

Using quadratic programming to estimate feature relevance in structural analyses of music 用二次规划估计音乐结构分析中的特征相关性

Proceedings of the 21st ACM international conference on Multimedia Pub Date : 2013-10-21 DOI: 10.1145/2502081.2502124

Jordan B. L. Smith, E. Chew

{"title":"Using quadratic programming to estimate feature relevance in structural analyses of music","authors":"Jordan B. L. Smith, E. Chew","doi":"10.1145/2502081.2502124","DOIUrl":"https://doi.org/10.1145/2502081.2502124","url":null,"abstract":"To identify repeated patterns and contrasting sections in music, it is common to use self-similarity matrices (SSMs) to visualize and estimate structure. We introduce a novel application for SSMs derived from audio recordings: using them to learn about the potential reasoning behind a listener's annotation. We use SSMs generated by musically-motivated audio features at various timescales to represent contributions to a structural annotation. Since a listener's attention can shift among musical features (e.g., rhythm, timbre, and harmony) throughout a piece, we further break down the SSMs into section-wise components and use quadratic programming (QP) to minimize the distance between a linear sum of these components and the annotated description. We posit that the optimal section-wise weights on the feature components may indicate the features to which a listener attended when annotating a piece, and thus may help us to understand why two listeners disagreed about a piece's structure. We discuss some examples that substantiate the claim that feature relevance varies throughout a piece, using our method to investigate differences between listeners' interpretations, and lastly propose some variations on our method.","PeriodicalId":20448,"journal":{"name":"Proceedings of the 21st ACM international conference on Multimedia","volume":"365 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83020444","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

GLocal structural feature selection with sparsity for multimedia data understanding 基于稀疏度的多媒体数据局部结构特征选择

Proceedings of the 21st ACM international conference on Multimedia Pub Date : 2013-10-21 DOI: 10.1145/2502081.2502142

Yan Yan, Zhongwen Xu, Gaowen Liu, Zhigang Ma, N. Sebe

{"title":"GLocal structural feature selection with sparsity for multimedia data understanding","authors":"Yan Yan, Zhongwen Xu, Gaowen Liu, Zhigang Ma, N. Sebe","doi":"10.1145/2502081.2502142","DOIUrl":"https://doi.org/10.1145/2502081.2502142","url":null,"abstract":"The selection of discriminative features is an important and effective technique for many multimedia tasks. Using irrelevant features in classification or clustering tasks could deteriorate the performance. Thus, designing efficient feature selection algorithms to remove the irrelevant features is a possible way to improve the classification or clustering performance. With the successful usage of sparse models in image and video classification and understanding, imposing structural sparsity in emph{feature selection} has been widely investigated during the past years. Motivated by the merit of sparse models, we propose a novel feature selection method using a sparse model in this paper. Different from the state of the art, our method is built upon $ell _{2,p}$-norm and simultaneously considers both the global and local (GLocal) structures of data distribution. Our method is more flexible in selecting the discriminating features as it is able to control the degree of sparseness. Moreover, considering both global and local structures of data distribution makes our feature selection process more effective. An efficient algorithm is proposed to solve the $ell_{2,p}$-norm sparsity optimization problem in this paper. Experimental results performed on real-world image and video datasets show the effectiveness of our feature selection method compared to several state-of-the-art methods.","PeriodicalId":20448,"journal":{"name":"Proceedings of the 21st ACM international conference on Multimedia","volume":"2013 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89615655","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: 16

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":"13 1","pages":""},"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

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

Session details: Annotation 会话详细信息:

Proceedings of the 21st ACM international conference on Multimedia Pub Date : 2013-10-21 DOI: 10.1145/3245300

Pablo Caesar

引用次数: 0

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

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

Session details: Best paper session 会议细节:最佳论文会议

Proceedings of the 21st ACM international conference on Multimedia Pub Date : 2013-10-21 DOI: 10.1145/3245285

R. Zimmerman

引用次数: 0