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Multimedia Data Mining and Analytics: Disruptive Innovation Softcover reprint of the original 1st ed. 2015 [Minkštas viršelis]

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  • Formatas: Paperback / softback, 454 pages, aukštis x plotis: 235x155 mm, weight: 7874 g, 153 Illustrations, color; 35 Illustrations, black and white; XIV, 454 p. 188 illus., 153 illus. in color., 1 Paperback / softback
  • Išleidimo metai: 05-Oct-2016
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3319347217
  • ISBN-13: 9783319347219
Kitos knygos pagal šią temą:
Multimedia Data Mining and Analytics: Disruptive Innovation Softcover reprint of the original 1st ed. 2015Didesnis paveikslėlis
  • Formatas: Paperback / softback, 454 pages, aukštis x plotis: 235x155 mm, weight: 7874 g, 153 Illustrations, color; 35 Illustrations, black and white; XIV, 454 p. 188 illus., 153 illus. in color., 1 Paperback / softback
  • Išleidimo metai: 05-Oct-2016
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3319347217
  • ISBN-13: 9783319347219
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9783319347219.html
Raktažodžiai:
This book provides fresh insights into the cutting edge of multimedia data mining, reflecting how the research focus has shifted towards networked social communities, mobile devices and sensors. The work describes how the history of multimedia data processing can be viewed as a sequence of disruptive innovations. Across the chapters, the discussion covers the practical frameworks, libraries, and open source software that enable the development of ground-breaking research into practical applications. Features: reviews how innovations in mobile, social, cognitive, cloud and organic based computing impacts upon the development of multimedia data mining; provides practical details on implementing the technology for solving real-world problems; includes chapters devoted to privacy issues in multimedia social environments and large-scale biometric data processing; covers content and concept based multimedia search and advanced algorithms for multimedia data representation, processing an

d visualization.

Part I: IntroductionDisruptive Innovation: Large Scale Multimedia Data MiningAaron K. Baughman, Jia-Yu Pan, Jiang Gao, and Valery A. PetrushinPart II: Mobile and Social Multimedia Data ExplorationSentiment Analysis Using Social MultimediaJianbo Yuan, Quanzeng You, and Jiebo LuoTwitter as a Personalizable Information ServiceMario Cataldi, Luigi Di Caro, and Claudio SchifanellaMining Popular Routes from Social MediaLing-Yin Wei, Yu Zheng, and Wen-Chih PengSocial Interactions over Location-Aware Multimedia SystemsYi Yu, Roger Zimmermann, and Suhua TangIn-house Multimedia Data MiningChristel Amato, Marc Yvon, and Wilfredo Ferre Content-based Privacy for Consumer-Produced MultimediaGerald Friedland, Adam Janin, Howard Lei, Jaeyoung Choi, and Robin SommerPart III: Biometric Multimedia Data ProcessingLarge-scale Biometric Multimedia ProcessingStefan van der Stockt, Aaron Baughman, and Michael PerlitzDetection of Demographics and Identity in Spontaneous Speec

h and WritingAaron Lawson, Luciana Ferrer, Wen Wang, and John MurrayPart IV: Multimedia Data Modeling, Search and EvaluationEvaluating Web Image Context ExtractionSadet Alcic and Stefan ConradContent Based Image Search for Clothing Recommendations in E-CommerceHaoran Wang, Zhengzhong Zhou, Changcheng Xiao, and Liqing ZhangVideo Retrieval based on Uncertain Concept Detection using Dempster-Shafer TheoryKimiaki Shirahama, Kenji Kumabuchi, Marcin Grzegorzek, and Kuniaki UeharaMultimodal Fusion: Combining Visual and Textual Cues for Concept Detection in VideoDamianos Galanopoulos, Milan Dojchinovski, Krishna Chandramouli, Toma s Kliegr, and Vasileios MezarisMining Videos for Features that Drive AttentionFarhan Baluch and Laurent IttiExposing Image Tampering with the Same Quantization MatrixQingzhong Liu, Andrew H. Sung, Zhongxue Chen, and Lei ChenPart V: Algorithms for Multimedia Data Presentation, Processing and VisualizationFast Binary Embedding for High-Dim

ensional DataFelix X. Yu, Yunchao Gong, and Sanjiv KumarFast Approximate K-Means via Cluster ClosuresJingdong Wang, Jing Wang, Qifa Ke, Gang Zeng, and Shipeng LiFast Neighborhood Graph Search using Cartesian ConcatenationJingdong Wang, Jing Wang, Gang Zeng, Rui Gan, Shipeng Li, and Baining GuoListen to the Sound of DataMark Last and Anna Usyskin (Gorelik)

Recenzijos

Multimedia data mining and analytics: disruptive innovation highlights new applications in multimedia data mining, presenting fascinating techniques together with comprehensive cases in practice. this book is valuable for the insight it provides related to the challenges faced by fast developing technologies, their current needs and future promise. It is a practical guide, a useful handbook for academies and industry practitioners who have interest in multimedia data analysis. (Shanshan Qi, Information Technology & Tourism, Vol. 16, 2016)

Part I: Introduction.- Disruptive Innovation: Large Scale Multimedia Data Mining.- Part II: Mobile and Social Multimedia Data Exploration.- Sentiment Analysis Using Social Multimedia.- Twitter as a Personalizable Information Service.- Mining Popular Routes from Social Media.- Social Interactions over Location-Aware Multimedia Systems.- In-house Multimedia Data Mining.- Content-based Privacy for Consumer-Produced Multimedia.- Part III: Biometric Multimedia Data Processing.- Large-scale Biometric Multimedia Processing.- Detection of Demographics and Identity in Spontaneous Speech and Writing.- Part IV: Multimedia Data Modeling, Search and Evaluation.- Evaluating Web Image Context Extraction.- Content Based Image Search for Clothing Recommendations in E-Commerce.- Video Retrieval based on Uncertain Concept Detection using Dempster-Shafer Theory.- Multimodal Fusion: Combining Visual and Textual Cues for Concept Detection in Video.- Mining Videos for Featuresthat Drive Attention.- Exposing Image Tampering with the Same Quantization Matrix.- Part V: Algorithms for Multimedia Data Presentation, Processing and Visualization.- Fast Binary Embedding for High-Dimensional Data.- Fast Approximate K-Means via Cluster Closures.- Fast Neighborhood Graph Search using Cartesian Concatenation.- Listen to the Sound of Data.

Aaron K. Baughman is a member of the Special Events Group at IBM (USA) for World Wide Sports. Previously, he was Technical Lead on a DeepQA Embed Research project that included an instance of the Jeopardy! Challenge.

Jiang (John) Gao is a Principal Scientist in the Advanced Development and Technology Group at Nokia USA, working on multimedia and mobile applications, data mining and computer vision.

Jia-Yu Pan is a software engineer at Google (USA), working on data mining and anomaly detection in big data.

Valery A. Petrushin is a Principal Scientist in the Research and Development Group at Opera Solutions (USA). His previous publications include the successful Springer title Multimedia Data Mining and Knowledge Discovery.