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Personalization in multimedia retrieval: A survey

Authors:

Qi TianAuthors Info & Claims

Multimedia Tools and Applications, Volume 51, Issue 1

Pages 247 - 277

https://doi.org/10.1007/s11042-010-0621-0

Published: 01 January 2011 Publication History

Abstract

With the explosive broadcast of multimedia (text documents, image, video etc.) in our life, how to annotate, search, index, browse and relate various forms of information efficiently becomes more and more important. Combining these challenges by relating them to user preference and customization only complicates the matter further. The goal of this survey is to give an overview of the current situation in the branches of research that are involved in annotation, relation and presentation to a user by preference. This paper will present some current models and techniques being researched to model ontology, preference, context, and presentation and bring them together in a chain of ideas that leads from raw uninformed data to an actual usable user interface that adapts with user preference and customization.

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Cited By

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https://dl.acm.org/doi/10.1145/3546155.3546701
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cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 51, Issue 1

January 2011

391 pages

ISSN:1380-7501

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Copyright © Copyright © 2011 Springer Science+Business Media, LLC.

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Kluwer Academic Publishers

United States

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Published: 01 January 2011

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Cited By

de Jesus Oliveira VRottermanner GGrößbacher SBoucher MJudmaier P(2022)Requirements and Concepts for Interactive Media Retrieval User InterfacesNordic Human-Computer Interaction Conference10.1145/3546155.3546701(1-10)Online publication date: 8-Oct-2022
https://dl.acm.org/doi/10.1145/3546155.3546701
Ma XYang XGao JXu C(2022)The Model May Fit You: User-Generalized Cross-Modal RetrievalIEEE Transactions on Multimedia10.1109/TMM.2021.309188824(2998-3012)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1109/TMM.2021.3091888
Li YLao LCui ZShan SYang J(2022)Graph Jigsaw Learning for Cartoon Face RecognitionIEEE Transactions on Image Processing10.1109/TIP.2022.317795231(3961-3972)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1109/TIP.2022.3177952
Goularte RTrojahn TKishi Rdos Santos JMuchaluat Saade Dda Graça C. Pimentel MMacedo A(2019)Multimedia information retrieval in big data using OpenCV pythonProceedings of the 25th Brazillian Symposium on Multimedia and the Web10.1145/3323503.3345030(25-27)Online publication date: 29-Oct-2019
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Trojahn TKishi RGoularte RNeto MNovais RFerraz CViana W(2018)A new multimodal deep-learning model to video scene segmentationProceedings of the 24th Brazilian Symposium on Multimedia and the Web10.1145/3243082.3243108(205-212)Online publication date: 16-Oct-2018
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Barbieri TTrojahn TPonti-Jr MGoularte RWainwright RCorchado JBechini AHong J(2015)Shot-HRProceedings of the 30th Annual ACM Symposium on Applied Computing10.1145/2695664.2695841(1257-1262)Online publication date: 13-Apr-2015
https://dl.acm.org/doi/10.1145/2695664.2695841
Wang SZhu YWu GJi Q(2014)Hybrid video emotional tagging using users' EEG and video contentMultimedia Tools and Applications10.1007/s11042-013-1450-872:2(1257-1283)Online publication date: 1-Sep-2014
https://dl.acm.org/doi/10.1007/s11042-013-1450-8
Trojahn TGoularte RPrazeres CSampaio PSantanchè ASantos CGoularte R(2013)Video scene segmentation by improved visual shot coherenceProceedings of the 19th Brazilian symposium on Multimedia and the web10.1145/2526188.2526206(23-30)Online publication date: 5-Nov-2013
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Wang BWang JLu H(2013)Exploiting content relevance and social relevance for personalized ad recommendation on internet TVACM Transactions on Multimedia Computing, Communications, and Applications10.1145/2501643.25016489:4(1-23)Online publication date: 19-Aug-2013
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