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

Multimodal analysis of user-generate...

Shah, Rajiv.

FindBook

Google Book

Amazon

博客來

Multimodal analysis of user-generated multimedia content

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Multimodal analysis of user-generated multimedia content/ by Rajiv Shah, Roger Zimmermann.
作者:	Shah, Rajiv.
其他作者:	Zimmermann, Roger.
出版者:	Cham :Springer International Publishing : : 2017.,
面頁冊數:	xxii, 263 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Multimedia data mining. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-61807-4
ISBN:	9783319618074

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.6..

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

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

3251475
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 2106020
003 DE-He213
005 20180312142247.0
006 m d
007 cr nn 008maaau
008 180417s2017 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 3251472
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 3251475
650 0 $a User-generated content. $3 931758
650 0 $a Social media. $3 786190
650 0 $a Multimedia systems. $3 517454
650 1 4 $a Biomedicine. $3 890831
650 2 4 $a Neurosciences. $3 588700
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Semantics. $3 520060
650 2 4 $a Cognitive Psychology. $3 891220
700 1 $a Zimmermann, Roger. $3 3251473
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Socio-affective computing ; $v v.6. $3 3251474
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-61807-4
950 $a Biomedical and Life Sciences (Springer-11642)