東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Multimedia data mining and analytics...

Baughman, Aaron K.

FindBook

Google Book

Amazon

博客來

Multimedia data mining and analytics = disruptive innovation /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Multimedia data mining and analytics/ edited by Aaron K. Baughman ... [et al.].
其他題名:	disruptive innovation /
其他作者:	Baughman, Aaron K.
出版者:	Cham :Springer International Publishing : : 2015.,
面頁冊數:	xiv, 454 p. :ill., digital ;24 cm.
內容註:	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 Features that 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.
Contained By:	Springer eBooks
標題:	Data mining. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-14998-1
ISBN:	9783319149981 (electronic bk.)

Multimedia data mining and analytics = disruptive innovation /
Multimedia data mining and analyticsdisruptive innovation /[electronic resource] :edited by Aaron K. Baughman ... [et al.]. - Cham :Springer International Publishing :2015. - xiv, 454 p. :ill., digital ;24 cm.

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 Features that 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.

This authoritative text/reference 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. Presenting a detailed exploration into the progression of the field, the book 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. Topics and features: Contains contributions from an international selection of pre-eminent authorities in the field. Reviews how disruptive 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 multimedia 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 and visualization The illuminating viewpoints presented in this comprehensive volume will be of great interest to researchers and graduate students involved in machine learning and pattern recognition, as well as to professional multimedia analysts and software developers.

ISBN: 9783319149981 (electronic bk.)

Standard No.: 10.1007/978-3-319-14998-1doiSubjects--Topical Terms:

562972
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Multimedia data mining and analytics = disruptive innovation /
LDR:03799nmm a2200313 a 4500 001 1997804
003 DE-He213
005 20151019140227.0
006 m d
007 cr nn 008maaau
008 151112s2015 gw s 0 eng d
020 $a 9783319149981 (electronic bk.)
020 $a 9783319149974 (paper)
024 7 $a 10.1007/978-3-319-14998-1 $2 doi
035 $a 978-3-319-14998-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D343
072 7 $a UG $2 bicssc
072 7 $a COM034000 $2 bisacsh
082 0 4 $a 006.312 $2 23
090 $a QA76.9.D343 $b M961 2015
245 0 0 $a Multimedia data mining and analytics $h [electronic resource] : $b disruptive innovation / $c edited by Aaron K. Baughman ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a xiv, 454 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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 Features that 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.
520 $a This authoritative text/reference 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. Presenting a detailed exploration into the progression of the field, the book 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. Topics and features: Contains contributions from an international selection of pre-eminent authorities in the field. Reviews how disruptive 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 multimedia 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 and visualization The illuminating viewpoints presented in this comprehensive volume will be of great interest to researchers and graduate students involved in machine learning and pattern recognition, as well as to professional multimedia analysts and software developers.
650 0 $a Data mining. $3 562972
650 0 $a Multimedia systems. $3 517454
650 1 4 $a Computer Science. $3 626642
650 2 4 $a Multimedia Information Systems. $3 892521
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Image Processing and Computer Vision. $3 891070
650 2 4 $a Signal, Image and Speech Processing. $3 891073
650 2 4 $a Innovation/Technology Management. $3 1565353
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
700 1 $a Baughman, Aaron K. $3 2139167
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-14998-1
950 $a Computer Science (Springer-11645)