Multimedia Tools and Applications

, Volume 62, Issue 1, pp 143–177 | Cite as

Experimenting with tagging and context for collaborative MPEG-7 metadata

  • Harry Agius
  • Marios C. Angelides
  • Damon Daylamani Zad
Article

Abstract

Whether unstructured or structured, tagging of multimedia resources is a laborious and time-consuming process when carried out in the context of a single individual. However, effort can be greatly reduced and the detail, quality and volume of metadata increased within the context of a web community. Despite this, little empirical research has been carried out to understand how users individually or collaboratively work with multimedia tagging tools, whether structured or unstructured. Consequently, we explore how to effectively achieve collaborative multimedia tagging through the results of an experiment that collected data from 51 users using both unstructured folksonomy (Flickr, YouTube and del.icio.us) and structured MPEG-7 tools (COSMOSIS). We contribute a detailed analysis of the use of the multimedia tagging tools used in the experiment and show the relationships between user behaviours, resultant outcomes of these behaviours, and subsequent implications for future collaborative multimedia MPEG-7 tagging tools.

Keywords

Metadata MPEG-7 Tagging Annotation Collaborative systems Folksonomy Experiment Multimedia 

References

  1. 1.
    Agius H, Angelides M (2006) MPEG-7 in action: end user experiences with COSMOS-7 front end systems. Proceedings of 21st Annual ACM Symposium on Applied Computing (SAC ’06) 2:1348–1355CrossRefGoogle Scholar
  2. 2.
    Agius H, Angelides M (2007) Closing the content-user gap in MPEG-7: the hanging basket model. Multimedia Systems 13(2):155–172CrossRefGoogle Scholar
  3. 3.
    Agius H, Angelides M (2009) From MPEG-7 user interaction tools to hanging basket models: bridging the gap. Multimedia Tools and Applications 41(3):375–406CrossRefGoogle Scholar
  4. 4.
    Al-Khalifa HS, Davis HC (2006) FolksAnnotation: A Semantic Metadata Tool for Annotating Learning Resources Using Folksonomies and Domain Ontologies. In: Proceedings of 3 rd International Conference on Innovations in Information Technology, pp. 1–5Google Scholar
  5. 5.
    Aurnhammer M, Hanappe P, Steels L (2006) Augmenting Navigation for Collaborative Tagging with Emergent Semantics Lecture Notes in Computer Science, Vol. 4273. Springer, Berlin / Heidelberg, pp 58–71Google Scholar
  6. 6.
    Begelman G, Keller P, Smadja F (2006) Automated Tag Clustering: Improving search and exploration in the tag space. In: Proceedings of Workshop on Collaborative Web Tagging. http://www.pui.ch/phred/automated_tag_clustering/
  7. 7.
    Blankinship E, Mikhak B (2007) Video-Wikis and Media Fluency. In: Proceedings of 6th International Conference on Interaction Design and Children, pp. 175–176Google Scholar
  8. 8.
    Carneiro G, Vasconcelos N (2005) Formulating semantic image annotation as a supervised learning problem. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2:163–168, vol. 162Google Scholar
  9. 9.
    Celebi E, Alpkoca A (2005) Combining textual and visual clusters for semantic image retrieval and auto-annotation. In: Proceedings of 2nd European Workshop on the Integration of Knowledge, Semantics and Digital Media Technology, pp. 219–225Google Scholar
  10. 10.
    Charmaz K (2006) Constructing Grounded Theory: A Practical Guide through Qualitative Analysis. Sage Publications Ltd, Newbury Park, CAGoogle Scholar
  11. 11.
    Corbin J, Strauss A (2008) Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory. Sage Publications Ltd., Newbury Park, CAGoogle Scholar
  12. 12.
    Cui Y, Jin JS, Zhang S, Luo S, Tian Q (2010) Music video affective understanding using feature importance analysis. In: Proceedings of ACM International Conference on Image and Video Retrieval, pp. 213–219Google Scholar
  13. 13.
    Dickerson JA, Kosko B (1993) Virtual worlds as fuzzy cognitive maps. In: Proceedings of IEEE Virtual Reality Annual International Symposium, 1993., pp. 471–477Google Scholar
  14. 14.
    Ding G, Wang J, Qin K (2010) A visual word weighting scheme based on emerging itemsets for video annotation. Information Processing Letters 110(16):692–696MATHCrossRefGoogle Scholar
  15. 15.
    Edvardsen LFH, Sølvberg IT, Aalberg T, Trætteberg H (2009) Automatically generating high quality metadata by analyzing the document code of common file types. In: Proceedings of 9th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 29–38Google Scholar
  16. 16.
    Golder SA, Huberman BA (2006) Usage patterns of collaborative tagging systems. Journal of Information Science 32(2):198–208CrossRefGoogle Scholar
  17. 17.
    Goulding C (1998) Grounded theory: the missing methodology on the interpretivist agenda. Qualitative Market Research 1(1):50–57CrossRefGoogle Scholar
  18. 18.
    Haslhofer B, Klas W (2010) A survey of techniques for achieving metadata interoperability. ACM Computing Surveys (CSUR) 42(2):1–37CrossRefGoogle Scholar
  19. 19.
    Hyun-seok M, De Neve W, Yong Man R (2010) Towards using semantic features for near-duplicate video detection. In: Proceedings of 2010 IEEE International Conference on Multimedia and Expo, pp. 1364–1369Google Scholar
  20. 20.
    ISO/IEC (2003) Information Technology − Multimedia Content Description Interface − Part 5: Multimedia Description Schemes. International Standard 15938-5, Geneva, SwitzerlandGoogle Scholar
  21. 21.
    ISO/IEC (2004) Information Technology − Multimedia Content Description Interface − Part 5: Multimedia Description Schemes: Amendment 2: Multimedia Description Schemes Extensions. International Standard 15938-5/Amd.2, Geneva, SwitzerlandGoogle Scholar
  22. 22.
    ISO/IEC (2005) Information Technology − Multimedia Content Description Interface − Part 5: Multimedia Description Schemes: Amendment 2: Multimedia Description Schemes User Preference Extensions. International Standard 15938-5/Amd.2, Geneva, SwitzerlandGoogle Scholar
  23. 23.
    Jung Y (2008) Influence of Sense of Presence on Intention to Participate in a Virtual Community. In: Proceedings of the 41st Annual Hawaii International Conference on System SciencesGoogle Scholar
  24. 24.
    Kosko B (1986) Fuzzy Cognitive Maps. International Journal of Man-Machine Studies 24:56–74CrossRefGoogle Scholar
  25. 25.
    Kosko B (1997) Fuzzy engineering. Prentice-Hall, Inc., Upper Saddle River, NJ, USAMATHGoogle Scholar
  26. 26.
    Kumar R, Tomkins A (2010) A characterization of online browsing behavior. In: Proceedings of 19th International Conference on World Wide Web, pp. 561–570Google Scholar
  27. 27.
    Kwon O (2009) A two-step approach to building bilateral consensus between agents based on relationship learning theory. Expert Systems with Applications 36(9):11957–11965CrossRefGoogle Scholar
  28. 28.
    Laborie S, Manzat A-M, Sédes F (2010) A generic framework for the integration of heterogeneous metadata standards into a multimedia information retrieval system. In: Proceedings of 9th International Conference on Adaptivity, Personalization and Fusion of Heterogeneous Information, pp. 80–83Google Scholar
  29. 29.
    Lee S-S, Yong H-S (2007) TagPlus: A Retrieval System using Synonym Tag in Folksonomy. In: Proceedings of International Conference on Multimedia and Ubiquitous Engineering (MUE ’07), pp. 294–298Google Scholar
  30. 30.
    Li Q, Lu SCY (2008) Collaborative Tagging Applications and Approaches. IEEE Multimedia 15(3):14–21CrossRefGoogle Scholar
  31. 31.
    Li Z, Shi Z, Liu X, Shi Z (2011) Modeling continuous visual features for semantic image annotation and retrieval. Pattern Recognition Letters 32(3):516–523CrossRefGoogle Scholar
  32. 32.
    Lu Y, Li Z-N (2008) Automatic object extraction and reconstruction in active video. Pattern Recogn 41(3):1159–1172CrossRefGoogle Scholar
  33. 33.
    Maleewong K, Anutariya C, Wuwongse, V (2008) A Collective Intelligence Approach to Collaborative Knowledge Creation. In: Proceedings of Fourth International Conference on Semantics, Knowledge and Grid, pp. 64–70Google Scholar
  34. 34.
    Matavire R, Brown I (2008) Investigating the use of “Grounded Theory” in information systems research. In: Proceedings of Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists on IT research in Developing Countries: Riding the Wave of Technology, pp. 139–147Google Scholar
  35. 35.
    Mayernik MS, Batcheller AL, Borgman CL (2011) How institutional factors influence the creation of scientific metadata. In: Proceedings of 2011 iConference, pp. 417–425Google Scholar
  36. 36.
    Mika P (2007) Ontologies are us: A unified model of social networks and semantics. Web Semantics: Science, Services and Agents on the World Wide Web 5(1):5–15MathSciNetCrossRefGoogle Scholar
  37. 37.
    Mishra S, Gorai A, Oberoi T, Ghosh H (2010) Efficient Visualization of Content and Contextual Information of an Online Multimedia Digital Library for Effective Browsing. Proceedings of 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology 3:257–260CrossRefGoogle Scholar
  38. 38.
    Naaman M, Nair R (2008) ZoneTag’s Collaborative Tag Suggestions: What is This Person Doing in My Phone? IEEE Multimedia 15(3):34–40CrossRefGoogle Scholar
  39. 39.
    Névéol A, Islamaj Dogan R, Lu Z (2011) Semi-automatic semantic annotation of PubMed queries: A study on quality, efficiency, satisfaction. Journal of Biomedical Informatics 44(2):310–318CrossRefGoogle Scholar
  40. 40.
    Ohmukai I, Hamasaki M, Takeda H (2005) A Proposal of Community-Based Folksonomy with RDF Metadata. In: Proceedings of 4th International Semantic Web Conference (ISWC). http://www-kasm.nii.ac.jp/papers/takeda/05/ohmukai05iswc.pdf
  41. 41.
    Ojokoh B, Zhang M, Tang J (2011) A trigram hidden Markov model for metadata extraction from heterogeneous references. Information Sciences 181(9):1538–1551MATHCrossRefGoogle Scholar
  42. 42.
    Otsuka I, Nakane K, Divakaran A, Hatanaka K, Ogawa M (2005) A highlight scene detection and video summarization system using audio feature for a personal video recorder. IEEE Transactions on Consumer Electronics 51(1):112–116CrossRefGoogle Scholar
  43. 43.
    Pea R, Lindgren R, Rosen J (2006) Computer-supported collaborative video analysis. In: Proceedings of 7th International Conference on Learning Sciences, pp. 516–521Google Scholar
  44. 44.
    Qiu J, Su J, Luo L, Zhou C (2008) The Application of Multimedia in Academic Library. In: Proceedings of 2008 International Conference on MultiMedia and Information Technology, pp. 703–706Google Scholar
  45. 45.
    Rodriguez MA, Bollen J, Sompel HVD (2009) Automatic metadata generation using associative networks. ACM Transactions on Information Systems (TOIS) 27(2):1–20CrossRefGoogle Scholar
  46. 46.
    Ryu J, Sohn Y, Kim M (2002) MPEG-7 metadata authoring tool. 10th ACM international conference on Multimedia. ACM, Juan-les-Pins, France 267–270Google Scholar
  47. 47.
    Saathoff C, Scherp A (2010) Unlocking the semantics of multimedia presentations in the web with the multimedia metadata ontology. In: Proceedings of 19th International Conference on World Wide Web, pp. 831–840Google Scholar
  48. 48.
    Shin Y, Kim Y, Kim EY (2010) Automatic textile image annotation by predicting emotional concepts from visual features. Image and Vision Computing 28(3):526–537CrossRefGoogle Scholar
  49. 49.
    Smoliar SW, HongJiang Z (1994) Content based video indexing and retrieval. IEEE Multimedia 1(2):62–72CrossRefGoogle Scholar
  50. 50.
    Tang L, Liu H, Zhang J, Agarwal N, Salerno JJ (2008) Topic taxonomy adaptation for group profiling. ACM Trans Knowl Discov Data 1(4):1–28CrossRefGoogle Scholar
  51. 51.
    Tao C, Embley DW (2009) Automatic hidden-web table interpretation, conceptualization, and semantic annotation. Data & Knowledge Engineering 68(7):683–703CrossRefGoogle Scholar
  52. 52.
    Ulges A, Schulze C, Keysers D, Breuel T (2008) A System That Learns to Tag Videos by Watching Youtube. Computer Vision Systems, pp. 415–424Google Scholar
  53. 53.
    Viitaniemi V, Laaksonen J (2007) Evaluating the performance in automatic image annotation: Example case by adaptive fusion of global image features. Signal Processing: Image Communication 22(6):557–568CrossRefGoogle Scholar
  54. 54.
    Voss J (2007) Tagging, Folksonomy & Co - Renaissance of Manual Indexing? In: Proceedings of 10th International Symposium for Information Science, pp. 234–254Google Scholar
  55. 55.
    William K (2006) Exploiting “The World is Flat” Syndrome in Digital Photo Collections for Contextual Metadata. In: Proceedings of Eighth IEEE International Symposium on Multimedia (ISM’06), pp. 341–347Google Scholar
  56. 56.
    Wu R-S, Li P-C (2011) Video annotation using hierarchical Dirichlet process mixture model. Expert Systems with Applications 38(4):3040–3048CrossRefGoogle Scholar
  57. 57.
    Yamamoto D, Masuda T, Ohira S, Nagao K (2008) Video Scene Annotation Based on Web Social Activities. IEEE Multimedia 15(3):22–32CrossRefGoogle Scholar
  58. 58.
    Yang D, Wu D, Koolmanojwong S, Brown AW, Boehm BW (2008) WikiWinWin: A Wiki Based System for Collaborative Requirements Negotiation. In: Proceedings of 41st Annual Hawaii International Conference on System Sciences, pp. 24–24Google Scholar
  59. 59.
    You J, Liu G, Perkis A (2010) A semantic framework for video genre classification and event analysis. Signal Processing: Image Communication 25(4):287–302CrossRefGoogle Scholar
  60. 60.
    Zhang X, Xu C, Cheng J, Lu H, Ma S (2009) Effective Annotation and Search for Video Blogs with Integration of Context and Content Analysis. IEEE Transactions on Multimedia 11(2):272–285CrossRefGoogle Scholar
  61. 61.
    Zhou H, Sadka AH, Swash MR, Azizi J, Sadiq UA (2010) Feature extraction and clustering for dynamic video summarisation. Neurocomputing 73(10–12):1718–1729CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Harry Agius
    • 1
  • Marios C. Angelides
    • 1
  • Damon Daylamani Zad
    • 1
  1. 1.Electronic and Computer Engineering, School of Engineering and DesignBrunel UniversityUxbridgeUK

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