New grand challenge for multimedia information retrieval: bridging the utility gap

International Journal of Multimedia Information Retrieval

Volumn 1, Issue 3, 2012, Pages 139-152

  Hanjalic, Alan ^a  

^a DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

Multimedia indexing; Multimedia information retrieval; Multimedia search; Utility by design

Indexed keywords

EID: 85015600536     PISSN: 21926611     EISSN: 2192662X     Source Type: Journal    
DOI: 10.1007/s13735-012-0019-z     Document Type: Article

References (79)

1. Ames M, Naaman M (2007) Why we tag: motivations for annotation in mobile and online media. ACM CHI
2. Boll S (2007) MultiTube—where multimedia and Web 2.0 could meet. IEEE Multimedia 14(1):9–13
3. Broder A (2002) A taxonomy of web search. ACM SIGIR Forum 36(2):3
4. Bu J, Tan S, Chen C, Wang C, Wu H, Zhang L, He X (2010) Music recommendation by unified hypergraph: combining social media information and music content. In: ACM Multimedia, pp 391–400
5. Casey MA, Veltkamp R, Goto M, Leman M, Rhodes C, Slaney M (2008) Content-based music information retrieval: current directions and future challenges. In: Hanjalic et al (eds) Proceedings of the IEEE special issue on advances in multimedia, information retrieval, vol 96, no. 4
6. Choi Y, Rasmussen EM (2003) Searching for images: the analysis of users’ queries for image retrieval in American history. J Am Soc Information Sci Technol 54(6):498–511
7. Clements M, de Vries AP, Reinders MJT (2010) The task-dependent effect of tags and ratings on social media access. ACM Trans Information Syst 28:21:1–21:42
8. Cremonesi P, Koren Y, Turrin R (2010) Performance of recommender algorithms on top-n recommendation tasks. In: ACM RecSys
9. Cronen-Townsend S, Zhou Y, Croft WB (2002) Predicting query performance. In: ACM SIGIR
10. Datta R, Joshi D, Li J, Wang JZ (April 2008) Image retrieval: ideas, influences, and trends of the new age. ACM Comput Surv 40(2):1–60
11. Davis M, King S, Good N, Sarvas R (2004) From context to content: leveraging context to infer media metadata. In: ACM Multimedia
12. Dietz R, Lang A (1999) Aefective agents: effects of agent affect on arousal, attention, liking and learning. In: Cognitive technology conference
13. Divakaran A (ed) (2009) Multimedia content analysis, theory and applications. Springer, Berlin. ISBN: 978-0-387-76567-9
14. Donato D, Donmez P, Noronha S (2011) Toward a deeper understanding of user intent and query expressiveness. In: ACM SIGIR, query representation and understanding workshop
15. Downie JS (2008) The music information retrieval evaluation exchange (2005–2007): a window into music information retrieval research. Acoust Sci Technol 29(4):247–255
16. Downie JS, Byrd D, Crawford T (2009) Ten years of ISMIR: reflections on challenges and opportunities. In: 10th International Society for music information retrieval conference (ISMIR)
17. Ebrahimi T (2009) Quality of multimedia experience: past, present and future. In: Keynote. ACM Multimedia
18. Fidel R (1997) The image retrieval task: implications for the design and evaluation of image databases. New Rev Hypermedia Multimedia 3
19. Greenwald MK, Cook EW, Lang PJ (1989) Affective judgment and psychophysiological response: dimensional covariation in the evaluation of pictorial stimuli. J Psychophysiol 3:51–64
20. Hanjalic A (2004) Content-based analysis of digital video. Kluwer, Dordrecht. ISBN: 1-4020-8114-6
21. Hanjalic A (March 2006) Extracting moods from pictures and sounds: towards truly personalized TV. IEEE Signal Process Mag 23(2):90–100
22. Hanjalic A (2007) Content you like anytime and anyplace: multimedia research for new TV concepts. In: Keynote at the Pacific Rim conference on multi-media
23. Hanjalic A, Lienhart R, Ma W-Y, Smith JR (eds) (2008) Proceedings of the IEEE special issue on advances in multimedia information retrieval, vol 96, no.4
24. Hanjalic A, Xu L-Q (2005) Affective video content representation and modeling. In: IEEE transactions on multimedia, pp 143–154
25. Hanjalic A (2012) A new gap to bridge: where to go next in social media retrieval? In: Keynote international conference on multimedia modeling, Klagenfurt
26. Hauff C, Murdock V, Yates RB (2008) Improved query difficulty prediction for the web. In: ACM CIKM
27. Hauptmann AG, Christel MG, Yan R (2008) Video retrieval based on semantic concepts. In: Proc IEEE special issue advance multimedia information retrieval, vol 96, no 4, pp 602-622
28. He J, Weerkamp W, Larson M, de Rijke M (2009) An effective coherence measure to determine topical consistency in user-generated content. Int J Document Anal Recogn 12(3):185–203
29. Hildebrand M, van Ossenbruggen J (2012) Linking user generated video annotations to the web of data. In: MMM, pp 693–704
30. Hsu W, Kennedy LS, Chang S-F (2007) Reranking methods for visual search. IEEE Multimedia 14(3):14–22
31. Huang TS, Dagli CK, Rajaram S, Chang EY, Mandel MI, Poliner GE, Ellis DPW (2008) Active learning for interactive multimedia retrieval. In: Proceedings of the IEEE special issue on advances in multimedia information retrieval, vol 96, no 4
32. Irie G, Satou T, Kojima A, Yamasaki T, Aizawa K (October 2010) Affective audio-visual words and latent topic driving model for realizing movie affective scene classification. IEEE Trans Multimedia 12(6):523–535
33. Jaimes A, Nagamine T, Liu J, Omura K, Sebe N (2005) Affective meeting video analysis. In: IEEE ICME
34. Jansen B, Booth D, Spink A (2008) Determining the informational, navigational and transactional intent of Web queries. Inf Proc Manag 44(3):1251–1266
35. Kang H-B (2003) Affective content detection using HMMs. ACM Multimedia
36. Kennedy LS, Naaman M (2008) Generating diverse and representative image search results for landmarks. In: Proceedings of WWW, pp 297–306
37. Lang P, Bradley M, Cuthbert B (2008) International affective picture system (IAPS): affective ratings of pictures and instruction manual. University of Florida, Gainesville, Tech. Rep. A-8
38. Larson M, Soleymani M, Eskevich M, Serdyukov P, Ordelman R, Jones G (2012) The community and the crowd: multimedia benchmark dataset development. IEEE Multimedia 19(3):2–10
39. Li X, Snoek CGM, Worring M (2008) Learning tag relevance by neighbor voting for social image retrieval. In: ACM MIR
40. Li Q, Lu SC-Y (2008) Collaborative tagging applications and approaches. IEEE Multimedia Mag 14–21 (July–September)
41. Liu D, Hua X-S, Yang L, Wang M, Zhang H-J (2009) Tag ranking. In: World Wide Web conference, pp 351–360
42. Lux M, Kofler C, Marques O (2010) A classification scheme for user intentions in image search. In: International conference human factors in computing systems
43. Ma W-Y (2011) Intent, knowledge, and the cloud: towards a new search paradigm. In: Keynote. International conference on multimedia modeling
44. Ma H, Lyu MR, King I (2009) Learning to recommend with trust and distrust relationships. In: ACM RecSys, pp 189–196
45. Massa P, Avesani P (2007) Trust-aware recommender systems. In: ACM RecSys, pp 17–24
46. Müller M, Ellis DPW, Klapuri A, Richard G Signal processing for music analysis. IEEE J Selected Topics Signal Process 5(6):1088–1110
47. Mahajan DK, Slaney M (2010) Image classification using the web graph. In: ACM international conference on multimedia, pp 991–994
48. Miyamori H, Nakamura S, Tanaka K (2005) Generation of views of TV content using TV viewers’ perspectives expressed in live chats on the web. ACM Press, ACM Multimedia, New York
49. Natsev A, Haubold A, Tesic J, Xie L, Yan R (2007) Semantic concept-based query expansion and re-ranking for multimedia retrieval: a comparative review and new approaches. In: ACM Multimedia
50. Over P, Awad G, Michel M, Fiscus J, Kraaij W, Smeaton AF (2012) TRECVID 2011—an overview of the goals, tasks, data, evaluation mechanisms and metrics. In: Proceedings of TRECVID 2011. NIST, March
51. Pantic M, Vinciarelli A (2009) Implicit human-centered tagging. IEEE Signal Process Mag 26
52. Paramita ML, Sanderson M, Clough P (2009) Diversity in photo retrieval: overview of the ImageCLEFPhoto task. In: CLEF
53. Ramzan N, Larson M, Dufaux F, Cluver K (2010) The participation payoff: challenges and opportunities for multimedia access in networked communities. In: ACM MIR
54. Rudinac S, Larson M, Hanjalic A (2010) Exploiting noisy visual concept detection to improve spoken content based video retrieval. In: ACM Multimedia
55. Rudinac S, Larson M, Hanjalic A (2011) Finding representative and diverse community contributed images to create visual summaries of geographic areas. In: ACM Multimedia
56. Sanderson M, Tang J, Arni T, Clough P (2009) What else is there? Search diversity examined. In: ECIR
57. Sigurbjörnsson B, van Zwol R (2008) Flickr tag recommendation based on collective knowledge. In: WWW, pp 327–336
58. Shan MK, Kuo FF, Chiang MF, Lee SY (September 2009) Emotion-based music recommendation by affinity discovery from film music. Expert Syst Appl 36(4):7666–7674
59. Shamma D, Kennedy L, Churchill EF (2009) Tweet the debates. In: ACM Multimedia workshop on social, media, pp 3–10
60. Shi Y, Larson M, Hanjalic A (2010) Towards understanding the challenges facing effective trust-aware recommendation. In: ACM RecSys ’10 workshop on recommender systems and the social web
61. Shi Y, Serdyukov P, Hanjalic A, Larson MA (2012) Personalized landmark recommendation based on geotags from photo sharing sites: towards alleviating data sparseness and making non-trivial recommendations. ACM Trans Intell Syst Technol (accepted)
62. Slaney M (2011) Web-scale multimedia analysis: does content matter? IEEE Multimedia 18(2):12–15
63. Slaney M (2011) Precision-recall is wrong for multimedia. IEEE Multimedia 18(3):4–7
64. Smeulders AWM, Worring M, Santini S, Gupta A, Jain R (2000) Content-based image retrieval at the end of the early years. IEEE Trans PAMI 22:1349–1380
65. Snoek CGM, Worring M (2008) Concept-based video retrieval. Foundations Trends Inf Retrieval 2(4):215–322
66. Smits EAP, Hanjalic A (2010) A system concept for socially enriched access to soccer video collections. IEEE Multimedia 17(4):26–34
67. Soleymani M, Kierkels JJM, Chanel G, Pun T (2009) A Bayesian framework for video affective representation. In: International conference on affective computing and intelligent interaction (ACII 2009) September
68. Soleymani M, Larson M (2010) Crowdsourcing for affective annotation of video: development of a viewer-reported boredom corpus. In: ACM SIGIR, pp 4–8
69. Song K, Tian Y, Gao W, Huang T (2006) Diversifying the image retrieval results. In: ACM Multimedia
70. Sun K, Yu J, Huang Y, Hu X (2009) An improved valence-arousal emotion space for video affective content representation and recognition. In: IEEE ICME
71. Tkalcic M, Burnik U, Kosir A (2010) Using affective parameters in a content-based recommender system for images. User modeling and user-adapted interaction. J Pers Res 20(4):1–33
72. von Ahn L (2006) Games with a purpose. IEEE Comput 39(6):96–98
73. van Leuken RH, Garcia L, Olivares X, van Zwol R (2009) Visual diversification of image search results. In: Proceedings of WWW, pp 341–350
74. Vliegendhart R, Larson M, Pouwelse J (2012) Discovering user perceptions of semantic similarity in near-duplicate multimedia files, CrowdSearch 2012 workshop. In: WWW, pp 259–262
75. Xu M, Jin JS, Luo S, Duan L (2008) Hierarchical movie affective content analysis based on arousal and valence features. ACM Multimedia, ACM Press, New York
76. Yazdani A, Kappeler K, Ebrahimi T (2011) Affective content analysis of music video clips. ACM Multimedia, MIRUM workshop. ACM Press, New York, pp 7–12
77. Yom-Tov E, Fine S, Carmel D, Darlow A (2005) Learning to estimate query difficulty. In: ACM SIGIR
78. Zha Z-J, Yang L, Mei T, Wang M, Wang Z (2009) Visual query suggestion. In: ACM Multimedia
79. Zhang S, Tian Q, Jiang S, Huang Q, Gao W (2008) Affective MTV analysis based on arousal and valence features. In: IEEE ICME