MS Pub Date : 2012-05-30 DOI: 10.1007/978-3-642-30433-0_10

Heechang Shin, R. Richardson, Oredola Soluade

引用次数: 5

Tapping the Potential of Virtual World-Based Simulations in Higher Education 挖掘虚拟世界模拟在高等教育中的潜力

MS Pub Date : 2012-05-30 DOI: 10.1007/978-3-642-30433-0_20

D. R. Moscato, S. Altschuller

引用次数: 0

Frequency Tuning Algorithm for Loudspeaker Driven Thermoacoustic Refrigerator Optimization 扬声器驱动热声制冷机优化的频率调谐算法

MS Pub Date : 2012-05-30 DOI: 10.1007/978-3-642-30433-0_27

Y. Kondratenko, O. Korobko, O. Kozlov

引用次数: 8

Mathematic Modeling of Reactor's Temperature Mode of Multiloop Pyrolysis Plant 多回路热解装置反应器温度模式的数学建模

MS Pub Date : 2012-05-30 DOI: 10.1007/978-3-642-30433-0_18

Y. Kondratenko, O. Kozlov

引用次数: 12

Division of Labour and Outsourcing: Mutually Exclusive Concepts? 劳动分工与外包:相互排斥的概念?

MS Pub Date : 2012-05-30 DOI: 10.1007/978-3-642-30433-0_16

W. Ukpere, M. S. Bayat

引用次数: 0

Spatio-temporal query for multimedia databases 多媒体数据库的时空查询

MS Pub Date : 2008-10-31 DOI: 10.1145/1460676.1460686

S. Wattamwar, Hiranmay Ghosh

引用次数: 20

Employing a photo's life cycle for multimedia retrieval 利用照片的生命周期进行多媒体检索

MS Pub Date : 2008-10-31 DOI: 10.1145/1460676.1460688

Philipp Sandhaus, Susanne CJ Boll, R. Fageth

引用次数: 2

Chants and Orcas: semi-automatic tools for audio annotation and analysis in niche domains 圣歌和虎鲸:在小众领域用于音频注释和分析的半自动工具

MS Pub Date : 2008-10-31 DOI: 10.1145/1460676.1460680

S. Ness, Matthew Wright, L. Martins, G. Tzanetakis

{"title":"Chants and Orcas: semi-automatic tools for audio annotation and analysis in niche domains","authors":"S. Ness, Matthew Wright, L. Martins, G. Tzanetakis","doi":"10.1145/1460676.1460680","DOIUrl":"https://doi.org/10.1145/1460676.1460680","url":null,"abstract":"The recent explosion of web-based collaborative applications in business and social media sites demonstrated the power of collaborative internet scale software. This includes the ability to access huge datasets, the ability to quickly update software, and the ability to let people around the world collaborate seamlessly. Multimedia learning techniques have the potential to make unstructured multimedia data accessible, reusable, searchable, and manageable. We present two different web-based collaborative projects: Cantillion, and the Orchive. Cantillion enables ethnomusicology scholars to listen and view data relating to chants from a variety of traditions, letting them view and interact with various pitch contour representations of the chant. The Orchive is a project to digitize over 20,000 hours of Orcinus orca (killer whale) vocalizations, recorded over a period of approximately 35 years, and provide tools to assist their study. The developed tools utilize ideas and techniques that are similar to the ones used in general multimedia domains such as sports video or news. However, their niche nature has presented us with special challenges as well as opportunities. Unlike more traditional domains where there are clearly defined objectives one of the biggest challenges has been the desire to support researchers to formulate questions and problems related to the data even when there is no clearly defined objective.","PeriodicalId":82053,"journal":{"name":"MS","volume":"9 1","pages":"9-16"},"PeriodicalIF":0.0,"publicationDate":"2008-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84803050","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

Multimedia knowledge management using ontologies 使用本体的多媒体知识管理

MS Pub Date : 2008-10-31 DOI: 10.1145/1460676.1460682

Antonio Penta, A. Picariello, L. Tanca

引用次数: 9

Affective ranking of movie scenes using physiological signals and content analysis 基于生理信号和内容分析的电影场景情感排序

MS Pub Date : 2008-10-31 DOI: 10.1145/1460676.1460684

M. Soleymani, G. Chanel, Joep J. M. Kierkels, T. Pun

{"title":"Affective ranking of movie scenes using physiological signals and content analysis","authors":"M. Soleymani, G. Chanel, Joep J. M. Kierkels, T. Pun","doi":"10.1145/1460676.1460684","DOIUrl":"https://doi.org/10.1145/1460676.1460684","url":null,"abstract":"In this paper, we propose an approach for affective ranking of movie scenes based on the emotions that are actually felt by spectators. Such a ranking can be used for characterizing the affective, or emotional, content of video clips. The ranking can for instance help determine which video clip from a database elicits, for a given user, the most joy. This in turn will permit video indexing and retrieval based on affective criteria corresponding to a personalized user affective profile.\u0000 A dataset of 64 different scenes from 8 movies was shown to eight participants. While watching, their physiological responses were recorded; namely, five peripheral physiological signals (GSR - galvanic skin resistance, EMG - electromyograms, blood pressure, respiration pattern, skin temperature) were acquired. After watching each scene, the participants were asked to self-assess their felt arousal and valence for that scene. In addition, movie scenes were analyzed in order to characterize each with various audio- and video-based features capturing the key elements of the events occurring within that scene.\u0000 Arousal and valence levels were estimated by a linear combination of features from physiological signals, as well as by a linear combination of content-based audio and video features. We show that a correlation exists between arousal- and valence-based rankings provided by the spectator's self-assessments, and rankings obtained automatically from either physiological signals or audio-video features. This demonstrates the ability of using physiological responses of participants to characterize video scenes and to rank them according to their emotional content. This further shows that audio-visual features, either individually or combined, can fairly reliably be used to predict the spectator's felt emotion for a given scene. The results also confirm that participants exhibit different affective responses to movie scenes, which emphasizes the need for the emotional profiles to be user-dependant.","PeriodicalId":82053,"journal":{"name":"MS","volume":"67 1","pages":"32-39"},"PeriodicalIF":0.0,"publicationDate":"2008-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84855705","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: 88

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