國立高雄大學圖資館 |

Language: English

Back

Multimodal analysis of user-generate...

Shah, Rajiv.

Multimodal analysis of user-generated multimedia content

Record Type:	Electronic resources : Monograph/item
Title/Author:	Multimodal analysis of user-generated multimedia contentby Rajiv Shah, Roger Zimmermann.
Author:	Shah, Rajiv.
other author:	Zimmermann, Roger.
Published:	Cham :Springer International Publishing :2017.
Description:	xxii, 263 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Multimedia data mining.
Online resource:	http://dx.doi.org/10.1007/978-3-319-61807-4
ISBN:	9783319618074$q(electronic bk.)

Multimodal analysis of user-generated multimedia content
Shah, Rajiv.

Multimodal analysis of user-generated multimedia content[electronic resource] /by Rajiv Shah, Roger Zimmermann. - Cham :Springer International Publishing :2017. - xxii, 263 p. :ill., digital ;24 cm. - Socio-affective computing,v.62509-5706 ;. - Socio-affective computing ;v.1..

This book presents a study of semantics and sentics understanding derived from user-generated multimodal content (UGC). It enables researchers to learn about the ways multimodal analysis of UGC can augment semantics and sentics understanding and it helps in addressing several multimedia analytics problems from social media such as event detection and summarization, tag recommendation and ranking, soundtrack recommendation, lecture video segmentation, and news video uploading. Readers will discover how the derived knowledge structures from multimodal information are beneficial for efficient multimedia search, retrieval, and recommendation. However, real-world UGC is complex, and extracting the semantics and sentics from only multimedia content is very difficult because suitable concepts may be exhibited in different representations. Moreover, due to the increasing popularity of social media websites and advancements in technology, it is now possible to collect a significant amount of important contextual information (e.g., spatial, temporal, and preferential information). Thus, there is a need to analyze the information of UGC from multiple modalities to address these problems. A discussion of multimodal analysis is presented followed by studies on how multimodal information is exploited to address problems that have a significant impact on different areas of society (e.g., entertainment, education, and journalism). Specifically, the methods presented exploit the multimedia content (e.g., visual content) and associated contextual information (e.g., geo-, temporal, and other sensory data). The reader is introduced to several knowledge bases and fusion techniques to address these problems. This work includes future directions for several interesting multimedia analytics problems that have the potential to significantly impact society. The work is aimed at researchers in the multimedia field who would like to pursue research in the area of multimodal analysis of UGC.

ISBN: 9783319618074$q(electronic bk.)

Standard No.: 10.1007/978-3-319-61807-4doiSubjects--Topical Terms:

790766
Multimedia data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Multimodal analysis of user-generated multimedia content
LDR:03004nmm a2200313 a 4500 001 520977
003 DE-He213
005 20180312142247.0
006 m d
007 cr nn 008maaau
008 180504s2017 gw s 0 eng d
020 $a 9783319618074$q(electronic bk.)
020 $a 9783319618067$q(paper)
024 7 $a 10.1007/978-3-319-61807-4 $2 doi
035 $a 978-3-319-61807-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D343
072 7 $a PSAN $2 bicssc
072 7 $a MED057000 $2 bisacsh
082 0 4 $a 006.312 $2 23
090 $a QA76.9.D343 $b S525 2017
100 1 $a Shah, Rajiv. $3 790764
245 1 0 $a Multimodal analysis of user-generated multimedia content $h [electronic resource] / $c by Rajiv Shah, Roger Zimmermann.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a xxii, 263 p. : $b ill., digital ; $c 24 cm.
490 1 $a Socio-affective computing, $x 2509-5706 ; $v v.6
520 $a This book presents a study of semantics and sentics understanding derived from user-generated multimodal content (UGC). It enables researchers to learn about the ways multimodal analysis of UGC can augment semantics and sentics understanding and it helps in addressing several multimedia analytics problems from social media such as event detection and summarization, tag recommendation and ranking, soundtrack recommendation, lecture video segmentation, and news video uploading. Readers will discover how the derived knowledge structures from multimodal information are beneficial for efficient multimedia search, retrieval, and recommendation. However, real-world UGC is complex, and extracting the semantics and sentics from only multimedia content is very difficult because suitable concepts may be exhibited in different representations. Moreover, due to the increasing popularity of social media websites and advancements in technology, it is now possible to collect a significant amount of important contextual information (e.g., spatial, temporal, and preferential information). Thus, there is a need to analyze the information of UGC from multiple modalities to address these problems. A discussion of multimodal analysis is presented followed by studies on how multimodal information is exploited to address problems that have a significant impact on different areas of society (e.g., entertainment, education, and journalism). Specifically, the methods presented exploit the multimedia content (e.g., visual content) and associated contextual information (e.g., geo-, temporal, and other sensory data). The reader is introduced to several knowledge bases and fusion techniques to address these problems. This work includes future directions for several interesting multimedia analytics problems that have the potential to significantly impact society. The work is aimed at researchers in the multimedia field who would like to pursue research in the area of multimodal analysis of UGC.
650 0 $a Multimedia data mining. $3 790766
650 0 $a User-generated content. $3 495453
650 0 $a Social media. $3 465899
650 0 $a Multimedia systems. $3 184435
650 1 4 $a Biomedicine. $3 273648
650 2 4 $a Neurosciences. $3 211508
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Semantics. $3 177636
650 2 4 $a Cognitive Psychology. $3 273717
700 1 $a Zimmermann, Roger. $3 790765
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Socio-affective computing ; $v v.1. $3 732529
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-61807-4
950 $a Biomedical and Life Sciences (Springer-11642)