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Title
Abstract
Introduction
Multimodal Analysis
Multimedia Input Analysis and Output Generation
Human Centered Methods
Conclusion
References
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Computer Science
Information Systems

Personalization in multimedia retrieval: A survey

Profile image of Ross HytnenRoss Hytnen

2010, Multimedia Tools and Applications

https://doi.org/10.1007/S11042-010-0621-0
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31 pages

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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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References (116)

  1. Agarwal S, Fankhauser P, Gonzalez-Ollala J, Hartman J, Hollfelder S, Jameson A, Klink S, Lehti P, Ley M, Rabbidge E, Scharzkopf E, Shrestha N, Stojanovic N, Studer R, Stumme G, Walter B, Weber A (2003) Semantic methods and tools for information portals. Proceedings of INFORMATIK 2003 -Innovative Informatikanwendungen, pp 116-131
  2. Agius H, Angelides M (2007) Closing the content-user gap in MPEG-7: the hanging basket model. Multimed Syst 13(2):155-176
  3. Ahn LV, Liu R, Blum M (2006) Peekaboom: a game for locating objects in images, SIGCHI Conference. Human Factors in Computing Systems, pp 55-64
  4. Aizawa K, Tancharoen D, Kawasaki S, Yamasaki T (2004) Efficient retrieval of life log based on context and content. ACM Workshop on Continuous Archival and Retrieval of Personal Experiences, pp 22-31
  5. Arifin S, Cheung PYK (2007) A computation method for video segmentation utilizing the pleasure- arousal-dominance emotional information. ACM Multimedia, pp 68-77
  6. Arthur GM, Harry A (2008) Video summarization: a conceptual framework and survey of the state of the art. J Vis Commun Image Represent 19(2):121-143
  7. Battelle J (2005) The search: how Google and its rivals rewrote the rules of business and transformed our culture, Portofolio Hardcover
  8. Belloti R, Decurtins C, Grossniklaus M, Norrie M, Palinginis A (2004) Modeling context for information environments, ubiquitous mobile information and collaboration systems. Lect Notes Comput Sci 3272:43-56
  9. Blei D, Jordan M (2003) Modeling annotated data. ACM SIGIR, pp 127-134
  10. Brewer E et al (2005) The case for technology in developing regions. IEEE Computer 38(6):25-38
  11. Bruno D, Denis L, Sharon O (2009) Multimodal interfaces: a survey of principles, models and frameworks, human machine interaction. Lect Notes Comput Sci 5440:3-26
  12. Bulterman D, Rutledge L (2004) SMIL 2.0: Interactive multimedia for web and mobile devices. Springer-Verlag, Heidelberg
  13. Bulterman D, Hardman L, Jansen J, Mullender K, Rutledge L (1998) GRiNS: A GRaphical interface for creating and playing SMIL documents. Comput Netw ISDN systems 10:519-529
  14. Chen L, Sycara K (1998) WebMate: personal agent for browsing and searching. Int. Conf. on Autonomous Agents, pp 132-139
  15. Chen H, Zheng NN, Liang L, Li Y, Xu YQ, Shum HY (2002) PicToon: a personalized image-based cartoon system, ACM Multimedia, pp 171-178
  16. Crystal D (1991) A dictionary of linguistics and phonetics. Blackwell, Oxford
  17. Deng J, Dong W, Socher R, Li J, Li K, Li FF (2009) ImageNet: a large-scale hierarchical image database. IEEE Conf. on Computer Vision and Pattern Recognition, pp 248-255
  18. Dimitrova N (2003) Multimedia content analysis: the next wave, Int. Conf. on Image and Video Retrieval, pp 415-420
  19. Dimitrova N, Zhang HJ, Shahraray B, Sezan I, Huang T, Zakhor A (2002) Applications of video- content analysis and retrieval. IEEE Multimedia 9(3):42-55
  20. Dorai C, Farrell R, Katriel A, Kofman G, Li Y, Park Y (2006) BMAGICAL demonstration: system for automated metadata generation for instructional content. ACM Multimedia, pp 491-492
  21. eHealth Workshop 2010, http://research.microsoft.com/en-us/collaboration/global/asia-pacific/pro- grams/ehealth.aspx
  22. Eynard D (2008) Using semantics and user participation to customize personalization, HP Laboratories Technical Report HPL-2008-197
  23. Fergus R, Perona P, Zissermann A (2003) Object class recognition by unsupervised scale invariant learning, IEEE Conf. on Computer Vision and Pattern Recognition, pp 264-271
  24. Foote JT (1997) Content-based retrieval of music and audio. SPIE Multimed Storage Archiving Syst II 3229:138-147
  25. Gevers T, Smeulders A (1999) Color based object recognition. Pattern Recogn 32:453-464
  26. Ghidini C, Giunchiglia F (2001) Local models, semantics, or contextual reasoning = locality + compatibility. Artif Intell 127(2):221-259
  27. Giunchiglia F, Serafini L (1994) Multilanguage hierarchical logics, or how can we do without modal logics. Artif Intell 65(1):29-70
  28. Guerts J, van OssenBruggen J, Hardman L (2001) Application-specific constraints for multimedia presentation generation. Int. Conf. on Multimedia Modelling, pp 247-266
  29. Guerts J, van OssenBruggen J, Hardman L, Rutledge L (2003) Towards a multimedia formatting vocabulary. Int. Conf. on WWW, pp 384-393
  30. Hanjalic A (2005) Adaptive extraction of highlights from a sport video based on excitement modeling. IEEE Trans Multimedia 7(6):1114-1122
  31. Hanjalic A (2006) Extracting moods from pictures and sounds: towards truly personalized TV. IEEE Signal Process Mag 23(2):90-100
  32. Hanjalic A, Xu LQ (2005) Affective video content representation and modeling. IEEE Trans Multimedia 7(1):143-154
  33. Hirsh H, Basu C, Davison B (2000) Learning to personalize. Commun ACM 43(8):102-106
  34. Hori T, Aizawa K (2003) Context-based video retrieval system for the Life Log applications. ACM Multimedia Information Retrieval Workshop, pp 31-38
  35. Hori T, Aizawa K (2004) Capturing life log and retrieval based on context. IEEE Conf. on Multimedia and Expo, pp 301-304
  36. Hua XS, Lu L, Zhang HJ (2004) P-Karaoke: personalized karaoke system, ACM Multimedia, pp 172-173
  37. Infomedia Project, http://www.informedia.cs.cmu.edu
  38. Isbister K, Hook K, Sharp M, Laaksolahti J (2006) The sensual evaluation instrument: developing an affective evaluation tool. SIGCHI Conf. on Human Factors in Computing Systems, pp 1163-1172
  39. Jaimes A, Sebe N (2007) Multimodal human-computer interaction: a survey. Comput Vis Image Underst 108(1-2):116-134
  40. Jaimes A, Sebe N, Gatica-Perez D (2006) Human-centered computing: a multimedia perspective, ACM Multimedia, pp 855-864
  41. Jaimes A, Gatica-Perez D, Sebe N, Huang T (2007) Human-centered computing: toward a human revolution. IEEE Computer 40(5):30-34
  42. Jain R (2003) Folk computing. Communications ACM 46(4):27-29
  43. Jameson A (2001) Systems that adapt to their users. Tutorial presented at IJCAI 2001, www.dfki.de/~jameson
  44. Jameson A (2001) User-adaptive and other smart adaptive systems: possible synergies. The First EUNITE Symposium, pp 13-14
  45. Kadlek T, Jelenik I (2008) Semantic user profile acquisition and sharing, Int. Conf. on Computer Systems and Technologies and Workshop for PhD students in Computing
  46. Kang HB (2002) Analysis of scene context related with emotional events. ACM Multimedia, pp 311-314
  47. Klemke R (2000) Context framework-an open approach to enhance organizational memory systems with context modeling techniques, Int. Conf. on Practical Aspects of Knowledge Management, pp 14- 1-14-12
  48. Lang PJ (1993) The network model of emotion: motivational connections. In: Advances in social cognition. Lawrence Erlbaum Associates, Hillsdale, NJ, pp 109-133
  49. Lavrenko V, Feng S, Manmatha R (2003) Statistical models for automatic video annotation and retrieval. Int. Conf. on Acoustics, Speech and Signal Processing, pp 17-21
  50. Lee M, Wilks Y (1996) An ascription-based approach to speech acts, Int. Conf. on Computational Linguistics, pp 699-704
  51. Lew M, Sebe N, Djeraba C, Jain R (2006) Content-based multimedia information retrieval: state-of-the- art and challenges. ACM Trans Multimed Comput Commun Appl 2(1):1-19
  52. Li T, Mitsunori O (2003) Detecting emotion in music. Int. Conf. on Music Information Retrieval (ISMIR), pp 239-240
  53. Li X, Yan J, Fan WG, Liu N, Yan SC, Chen Z (2009) An online blog reading system by topic clustering and personalized ranking. ACM Trans. on Internet Technology 9(3) Article 9
  54. Liu D, Lu L, Zhang HJ (2003) Automatic mood detection from acoustic music data. Int. Conf. on Music Information Retrieval (ISMIR), pp 81-87
  55. Liu B, Gupta A, Jain R (2005) MedSMan: a streaming data management system over live multimedia, ACM Multimedia, pp 171-180
  56. Liu D, Hua G, Viola P, Chen T (2008) Integrated feature selection and higher-order spatial feature extraction for object categorization. IEEE Conf. on Computer Vision and Pattern Recognition, pp 1-8
  57. Lu L, Liu D, Zhang HJ (2006) Automatic mood detection and tracking of music audio signals. IEEE Trans Audio Lang Process 14(1):5-18
  58. Magnini B, Strapparava C (2004) User modeling for news web sites with word sense based techniques. User Model User-Adapt Interact 14(2-3):239-257
  59. Mann W, Matthiesen C, Thompson S (1989) Rhetorical structure theory and text analysis, technical report ISI/RR-89-242, November
  60. Marszalek M, Schmid C (2006) Spatial weighting for bag-of-features. IEEE Conf. on Computer Vision and Pattern Recognition, pp 2118-2125
  61. Maybury MT (1997) Intelligent multimedia information retrieval, AAAI/MIT Press
  62. McCarthy J (1987) Generality in artificial intelligence. Commun ACM 30(12):1030-1035
  63. Mehrabian A (1996) Pleasure-arousal-dominance: a general framework for describing and measuring individual differences in temperament. Curr Psycho 14(4):261-292
  64. Mikolajczyk K, Schmid C (2004) Scale and affine invariant interest point detectors. Int J Comp Vis 60:63-86
  65. Moncrieff S, Dorai C, Venkatesh S (2001) Affect computing in film through sound energy dynamics. ACM Multimedia, pp 525-527
  66. MPEG-Moving Picture Expert Group, http://www.chiariglione.org/mpeg/
  67. Naphade, Huang TS (2001) A probabilistic framework for semantic video indexing, filtering and reieval. IEEE Trans Multimedia 3(1):141-151
  68. Naphade MR, Huang TS (2002) Extracting semantics from audiovisual content: the final frontier in multimedia retrieval. IEEE Trans Neural Netw 13(4):793-810
  69. Naphade MR, Kristjansson T, Frey B, Huang TS (1998) Probabilistic multimedia objects (Multijects): a novel approach to video indexing and retrieval in multimedia systems. Int. Conf. on Image Processing, pp 536-540
  70. Nister D, Stewenius H (2006) Scalable recognition with a vocabulary tree, IEEE Conf. on Computer Vision and Pattern Recognition, pp 2161-2168
  71. Oviatt S (2003) User-centered modeling and evaluation of multimodal interfaces. Proc IEEE 91 (9):1457-1468
  72. Parsons S, Sierra C, Jennings NR (1998) Agents that reason and negotiate by arguing. J Log Comput 8 (3):261-292
  73. Quiroga L (1999) Empirical evaluation of explicit vs implicit acquisition of user profiles in information filtering systems, ACM Conf. on Digital Libraries, pp 238-239
  74. Rauber A, Pampalk E, Merkl D (2003) The SOM-enhanced jukebox: organization and visualization of music collections based on perceptual models. J New Music Res JNMR 32(2):193-210
  75. Rigo S, Jose O (2008) Advanced in conceptual modeling-challenges and opportunities: ER 2008 Workshops CMLSA, ECDM, FP-UML, M2AS, RIGiM, SeCoGIS, WISM. Lect Notes Comput Sci 5232
  76. Roy D, Pentland A (2002) Learning words from sights and sounds: a computational model. Cogn Sci 26(1):113-146
  77. Russell J, Mehrabian A (1977) Evidence for a three-factor theory of emotions. J Res Pers 11:273- 294
  78. Savarese S, Winn J, Criminisi A (2006) Discriminative object class models of appearance and shape by correlatons. IEEE Conf. on Computer Vision and Pattern Recognition, pp 2033-2040
  79. Schilit B, Adams N, Want R (1994) Context-aware computing applications. IEEE Workshop on Mobile Computing Systems and Applications, pp 85-90
  80. Schlosberg H (1954) Three dimensions of emotion. Psychol Rev 61(2):81-88
  81. Sebe N, Tian Q (2007) Personalized multimedia retrieval: the new trend? ACM Multimedia Information Retrieval Workshop, pp 299-306
  82. Zhang S, Huang Q, Jiang S, Gao W, Tian Q (2010) Affective visualization and retrieval for music video. IEEE Trans Multimedia, Special Issue on Multimodal Afftective Interaction 12 (6):510-522
  83. Sivic J, Zisserman A (2003) Video Google: a text retrieval approach to object matching in videos, Int. Conf. on Computer Vision, pp 1470-1477
  84. Smeulders A, Worring M, Santini S, Gupta A, Jain R (2000) Content based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 22(12):1349-1380
  85. Snoek CGM, Worring M, Geusebroek J, Koelma D, Seinstra F, Smeulders A (2006) The semantic pathfinder: using an authoring metaphor for generic multimedia indexing. IEEE Trans Patt Anal Mach Intell 28(10):1678-1689
  86. Song Y, Hua XS, Dai LR, Wang M (2005) Semi-automatic video annotation based on active learning with multiple complementary predictors. ACM Int. Workshop on Multimedia Information Retrieval, pp 97-104
  87. StreamSage, http://www.streamsage.com
  88. Sullivan DO, Smyth B, Wilson DC, McDonald K, Smeaton A (2004) Improving the quality of the personalized electronic program guide. User Model User-Adapt Interact 14(1):5-36
  89. Torralba A, Fergus R, Freeman WT (2008) 80 million tiny images: a large dataset for non-parametric object and scene recognition. IEEE Trans Pattern Anal Mach Intell 30(11):1958-1970
  90. Tseng BL, Lin CY, Smith JR (2004) Using MPEG-7 and MPEG-21 for personalizing video. IEEE Trans Multimedia 11(1):42-52
  91. Tsinaraki C, Christodoulakis S (2005) Semantic user preference descriptions in MPEG-7/21. The 4th Hellienic Data Managerment Symposium (HDMS)
  92. Tsinaraki C, Christodoulakis S (2006) A multimedia user preference model that supports semantics and its application to MPEG 7/21. Int. Conf. on Multimedia Modelling, pp 35-42
  93. Tsinaraki C, Polydoros P, Kazasis F, Christodoulakis S (2005) Ontology-based semantic indexing for MPEG-7 and TV-anytime audiovisual content. Multimed Tools Appl 26(3):299-325
  94. Venkatesh S, Adams B, Phung D, Dorai C, Farrell RG, Agnihotri L, Dimitrova N (2008) "You Tube and I Find"-personalizing multimedia content access. Proc IEEE 96(4):697-711
  95. Wang HL, Cheong LF (2006) Affective understanding in film. IEEE Trans Circuits Syst Video Technol 16(6):689-704
  96. Wang FS, Lu W, Liu J, Shah M, Xu D (2008) Automatic video annotation with adaptive number of key words, Int. Conf. on Pattern Recognition, pp 1-4
  97. Wang F, Jiang YG, Ngo CW (2008) Video event detection using motion relativity and visual relatedness. ACM Multimedia, pp 239-248
  98. Webb GI, Pazzani MJ, Billsus D (2001) Machine learning for user modeling. User Model User-Adapt Interact 11(1-2):19-29
  99. Wei G, Petrushin V, Gershman A (2002) From data to insight: the community of multimedia agents, Int. Workshop on Multimedia Data Mining
  100. Weitzman L, Wittenberg K (1994) Automatic presentation of multimedia documents using relational grammars. ACM Multimedia, pp 443-451
  101. Winn J, Criminisi A, Minka T (2005) Object categorization by learning universal visual word dictionary. Int. Conf. on Computer Vision, pp 1800-1807
  102. Wold E, Blum T, Kreislar D, Wheaton J (1996) Content-based classification, search, and retrieval of audio. IEEE Multimedia 3(3):27-36
  103. Xu D, Chang SF (2008) Video event recognition using kernel methods with multilevel temporal alignment. IEEE Trans Pattern Anal Mach Intell 30(11):1985-1997
  104. Xu M, Chia LT, Jin J (2005) Affective content analysis in comedy and horror videos by audio emotional event detection. IEEE Int. Conf. on Multimedia and Expo, pp 622-625
  105. Yang L, Meer P, Foran DJ (2007) Multiple class segmentation using a unified framework over mean- shift patches. IEEE Conf. on Computer Vision and Pattern Recognition, pp 1-8
  106. Yu B, Ma WY, Nahrstedt K, Zhang HJ (2003) Video summarization based on user log enhanced link analysis. ACM Multimedia, pp 382-391
  107. Zeng ZH, Pantic M, Roisman GI, Huang T. A survey of affect recognition methods: audio, visual and spontaneous expressions. IEEE Trans Pattern Anal Mach Intell 31(1):39-58
  108. Zhang S, Tian Q, Hua G, Huang Q, Li S (2009) Descriptive visual words and visual phrases for image applications. ACM Multimedia, pp 75-84
  109. Zhou M (1999) Visual planning: a practical approach to automated presentation design. Int. Joint Conference on Artificial Intelligence, pp 634-641
  110. Zhou XS, Huang TS (2003) Relevance feedback in image retrieval: a comprehensive review. Multimed Syst 8(6):536-544
  111. Zhou M, Houck K, Pan S, Shaw J, Aggarwal V, Wen Z (2006) Enabling context-sensitive information seeking, Int. Conf. on Intelligent User Interfaces, pp 116-123
  112. Zhou X, Zhuang XD, Yan SC, Chang SF, Johnson MH, Huang T (2008) SIFT-Bag kernel for video event analysis. ACM Multimedia, pp 229-238
  113. Von AL (2006) Games with a purpose. IEEE Computer 39(6):96-98
  114. Yijuan Lu is an Assistant Professor in the Department of Computer Science, Texas State University. She received her Ph.D. in CS in 2008 from the University of Texas at San Antonio. During 2006, 2007, 2008, she was a summer Intern Researcher at FXPAL lab, Web Search & Mining Group, Microsoft Research Asia (MSRA), National Resource for Biomedical Supercomputing (NRBSC) at the Pittsburgh Supercomputing Center (PSC), Pittsburgh. She was the Intern Researcher at Media Technologies Lab, Hewlett-Packard Laboratories (HP) 2008, and research fellow of Multimodal Information Access and Synthesis (MIAS) Center at University of Illinois at Urbana-Champaign (UIUC) 2007. Her current research interests include Multimedia Information Retrieval, Computer Vision, Machine Learning, Data Mining, and Bioinformatics. She has published extensively and serves as reviewers at top conferences and journals. She is the 2007 Best Paper Candidate in Retrieval Track of Pacific-rim Conference on Multimedia (PCM) and the recipient of 2007 Prestigious HEB Dissertation Fellowship, 2007 Star of Tomorrow Internship Program of MSRA, She is a member of IEEE and ACM.
  115. Nicu Sebe received his PhD degree in 2001 from University of Leiden, The Netherlands. He is with the Faculty of Cognitive Sciences, University of Trento, Italy, where he is leading the research in the areas of multimedia information retrieval and human-computer interaction in computer vision applications. He is the author of Robust Computer Vision-Theory and Applications (Kluwer, April 2003) and of Machine Learning in Computer Vision (Springer, May 2005). He was involved in the organization of the major conferences and workshops addressing the computer vision and human-centered aspects of multimedia information retrieval, among which as a General Co-Chair of the IEEE Automatic Face and Gesture Recognition Conference, FG 2008, ACM International Conference on Image and Video Retrieval (CIVR) 2007, and WIAMIS 2009 and as one of the initiators and a Program Co-Chair of the Human-Centered Multimedia track of the ACM Multimedia 2007 conference. He is the general chair of ACM CIVR 2010 and a track chair of ICPR 2010. He has served as the guest editor for several special issues in IEEE Computer, Computer Vision and Image Understanding, Image and Vision Computing, Multimedia Systems, and ACM TOMCCAP. He has been a visiting professor in Beckman Institute, University of Illinois at Urbana- Champaign and in the Electrical Engineering Department, Darmstadt University of Technology, Germany. He was the recipient of a British Telecomm Fellowship. He is the co-chair of the IEEE Computer Society Task Force on Human-centered Computing and is an associate editor of Machine Vision and Applications, Image and Vision Computing, Electronic Imaging and of Journal of Multimedia.
  116. Ross Hytnen is an Engineering Scientist at the Applied Research Labs of the University of Texas and will soon take a position as lead senior software developer at NanoMetrics in Austin, Texas. He is currently completing his masters in CS in 2011 from Texas State University. His current research interests include image recognition, multimedia information retrieval, and sonar image construction.

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Miriam Fernandez

2006 IEEE International Symposium on Circuits and Systems

We discuss the use of a reliable user requirements methodology for gathering essential data relating to user needs in advanced, personalised multimedia content applications. We revise the implications of these requirements in the development of advanced personalised searching and browsing tools aimed at assisting the end-consumer by complementing explicit user requests with implicit user preferences, to better meet individual user needs. We examine the important technical challenges such as representing conceptual-level user interests, and coping with the subtleties of user preferences, such as variability and heterogeneity, which we see as critical for the success of personalisation techniques. We consider the fact that personalisation is not always appropriate, and it is sometimes preferable to disable it to avoid obtrusiveness.

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Related topics

  • Information Systems
  • Computer Science
  • Information Retrieval
  • Ontology
  • Multimedia Retrieval
  • User preferences
  • Informational Efficiency
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