Visual image retrieval

Annual Review of Information Science and Technology

Volumn 42, Issue , 2008, Pages 1-42

  Enser, Peter G B ^a,b,c,d,e  

^a UNIVERSITY OF BRIGHTON   (United Kingdom)

^b Mathematical and Information Sciences

^c Institute of Information Scientists

^d Chartered Institute of Library and Information Professionals

^e Arts and Humanities Research Council   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 38349054152     PISSN: 00664200     EISSN: None     Source Type: Book Series    
DOI: 10.1002/aris.2008.1440420108     Document Type: Review

References (143)

1. Addis, M. J., Martinez, K., Lewis, P., Stevenson, J., & Giorgini, F. (2005). New ways to search, navigate and use multimedia museum collections over the Web. Proceedings of Museums and the Web 2005. Retrieved January 7, 2007, from www.archimuse.com/mw2005/papers/addis/addis.html
2. Alwis, S., & Austin, J. (1998). A novel architecture for trademark image retrieval systems. In J. P. Eakins, D. J. Harper, & J. Jose (Eds.), The Challenge of Image Retrieval: Workshop on Image Retrieval, University of Northumbria, Newcastle upon Tyne, U.K. Retrieved January 24, 2007, from www.bcs.org/upload/pdf/ewic_im98_paper10.pdf
3. Angelini, S. (Ed.). (2006). The researcher's guide: Film, television, radio and related documentation collections in the UK (7th ed.). London: British Universities Film and Video Council.
4. Armitage, L. H., & Enser, P. G. B. (1997). Analysis of user need in image archives. Journal of Information Science, 23(4), 287-299.
5. Baca, M., & Harpring, P. (Eds.). (2005). Categories for the description of works of art. Los Angeles: J. Paul Getty Trust & College Art Association. Retrieved January 7, 2007, from www.getty.edu/research/ conducting_research/standards/cdwa
6. Baillie, M., & Jose, J. (2003). Audio-based event detection for sports video. Image and video retrieval: Proceedings of the Second International Conference (Lecture Notes in Computer Science, 2728), 300-309.
7. Bakewell, E., Beeman, W. O., & Reese, C. M. (1988). Object-image-inquiry: The art historian at work (M. Schmitt, Ed.). Santa Monica, CA: Getty Art History Information Program.
8. Barnard, K., Duygulu, P., Forsyth, D., de Freitas, N., Blei, D. M., & Jordan, M. I. (2003). Matching words and pictures. Journal of Machine Learning Research, 3, 1107-1135.
9. Berry, M. W., Dumais, S. T., & O'Brien, S. T. (1995). Using linear algebra for intelligent information retrieval. SIAM Review, 37(4), 573-595.
10. Besser, H. (1990). Visual access to visual images: The UC Berkeley Image Database Project. Library Trends, 38(4), 787-798.
11. Boll, S. (2005). Image and video retrieval from a user-centered mobile multimedia perspective. Image and Video Retrieval: Proceedings of the Fourth International Conference (Lecture Notes in Computer Science, 3568), 18-27.
12. British Association of Picture Libraries and Agencies. (2004). Directory of picture libraries and agencies. London: The Association.
13. British Broadcasting Corporation. (2006). BBC news online, 10 October 2006. Retrieved January 7, 2007, from news.bbc.co.uk/1/hi/business/6034577. stm
14. British Broadcasting Corporation. (2007). BBC motion gallery about us. Retrieved January 7, 2007, from www.bbcmotiongallery.com/Customer/ AboutUs.aspx
15. Brodatz, P. (1966). Textures: A photographic album for artists and designers. New York: Dover.
16. Brunskill, J., & Jörgensen, C. (2002). Image attributes: A study of scientific diagrams. Proceedings of the Annual Meeting of the American Society for Information Science and Technology, 365-375.
17. Cao, Y., Tavanapong, W., Kim, K., & Oh, J. (2003). Audio-assisted scene segmentation for story browsing. Image and video retrieval: Proceedings of the Second International Conference (Lecture Notes in Computer Science, 2728), 446-455.
18. Carnegie Mellon University. (2007). Informedia digital video library. Retrieved January 7, 2007, from www.informedia.cs.cmu.edu
19. Cawkell, A. E. (1992). Selected aspects of image processing and management: Review and future prospects. Journal of Information Science, 18(3), 179-192.
20. Chang, S.-F., Smith, J. R., Beigi, M., & Benitez, A. (1997). Visual information retrieval from large distributed online repositories. Communications of the ACM, 40(12), 63-71.
21. Chen, H. (2001). An analysis of image queries in the field of art history. Journal of the American Society for Information Science and Technology, 52(3), 260-273.
22. Choi, Y., & Rasmussen, E. M. (2002). Users' relevance criteria in image retrieval in American history. Information Processing & Management, 38(5), 695-726.
23. Christel, M. G., & Conescu, R. M. (2005). Addressing the challenge of visual information access from digital image and video libraries. In Proceedings of the 5th ACM/IEEE-CS Joint Conference on Digital Libraries, 69-78.
24. Christel, M. G., & Conescu, R. M. (2006). Mining novice user activity with TRECVID interactive retrieval tasks. Image and Video Retrieval: Proceedings of the 5th International Conference (Lecture Notes in Computer Science, 4071), 21-30.
25. Davis, M. (2003). Editing out video editing. IEEE MultiMedia Archive, 10(2), 54-64.
26. Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391-407.
27. Del Bimbo, A. (1999). Visual information retrieval. San Francisco: Morgan Kaufmann.
28. Delp, E. (2005, November). Are low level features too low for indexing? Keynote address presented at the 2nd European Workshop on the Integration of Knowledge, Semantics and Digital Media Technology, London.
29. Dimitrova, N., Barbieri, M., & Agnihotri, L. (2004). Providing rapid access to video content through automatic summaries. In R. Raeli & P. Innocenti (Eds.), MMIR MultiMedia Information Retrieval: Metodologie ed esperienze internazionali di content-based retrieval per l'informazione e la documentazione (pp. 281-300). Rome: AIDA.
30. Dublin Core Metadata Initiative. (2006). The Dublin Core Metadata Initiative. Retrieved January 7, 2007, from dublincore.org
31. Dumais, S. T. (2004). Latent semantic analysis. Annual Review of Information Science and Technology, 38(1), 188-230.
32. Duygulu, P., Barnard, K., de Freitas, J. F. G., & Forsyth, D. A. (2002). Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. Proceedings of the 7th European Conference on Computer Vision - Part IV (Lecture Notes in Computer Science, 2353), 97-112.
33. Eakins, J. P. (1993). Design criteria for a shape retrieval system. Computers in Industry, 21(2), 167-184.
34. Eakins, J. P. (2002). Towards intelligent image retrieval. Pattern Recognition, 35(1), 3-14.
35. Eakins, J. P., & Graham, M. E. (1999). Content-based image retrieval: A report to the JISC Technology Applications Programme. Newcastle upon Tyne, UK: Institute for Image Data Research, University of Northumbria at Newcastle.
36. Eakins, J. P., Graham, M. E., Boardman, J. M., & Shields, K. (1996). Retrieval of trade mark images by shape feature (British Library Research and Innovation Report 26). London: British Library Research and Innovation Centre.
37. Edina. (2007). Education image gallery. Retrieved January 7, 2007, from edina.ac.uk
38. Enser, P. G. B. (1993). Query analysis in a visual information retrieval context. Journal of Document and Text Management, 1(1), 25-52.
39. Enser, P. G. B. (1995). Pictorial information retrieval. Journal of Documentation, 51(2), 126-170.
40. Enser, P. G. B. (2000). Visual image retrieval: Seeking the alliance of concept-based and content-based paradigms. Journal of Information Science, 26(4), 199-210.
41. Enser, P. G. B., & McGregor, C. G. (1992). Analysis of visual information retrieval queries. Report on Project G16412 to the British Library Research & Development Department. London: The Department.
42. Enser, P. G. B., Sandom, C. J., Hare, J. S., & Lewis, P. H. (in press). Facing the reality of semantic image retrieval. Journal of Documentation.
43. Enser, P. G. B., Sandom, C. J., & Lewis, P. H. (2005a). Automatic annotation of images from the practitioner perspective. Image and video retrieval: Proceedings of the Fourth International Conference (Lecture Notes in Computer Science, 3568), 497-506.
44. Enser, P. G. B., Sandom, C. J., & Lewis, P. H. (2005b). Surveying the reality of semantic image retrieval. In S. Bres & R. Laurini (Eds.), Visual Information and Information Systems: 8th International Conference, revised selected papers (Lecture Notes in Computer Science, 3736) (pp. 177-188). Berlin: Springer-Verlag.
45. Evans, H., & Evans, M. (2003). Picture researcher's handbook (7th ed.). Leatherhead, UK: PIRA.
46. Fidel, R. (1997). The image retrieval task: Implications for the design and evaluation of image databases. The New Review of Hypermedia and Multimedia, 3, 181-199.
47. Film Images (London) Ltd. (2005). Film images advanced search. Retrieved January 7, 2007, from www.film-images.com/advance_search.jsp
48. Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Huang, Q., Dom, B., et al. (1995). Query by image and video content: The QBIC system. IEEE Computer, 28(9), 23-32.
49. Frost, C. O., & Noakes, A. (1998). Browsing images using broad classification categories. Proceedings of the 9th ASIS SIGCR Classification Research Workshop, 71-89.
50. Goodrum, A., Bejune, M., & Siochi, A. C. (2003). A state transition analysis of image search patterns on the Web. Image and Video Retrieval: Proceedings of the Second International Conference (Lecture Notes in Computer Science, 2728), 281-290.
51. Goodrum, A., & Spink, A. (2001). Image searching on the Excite Web search engine. Information Processing & Management, 37(2), 295-311.
52. Google. (2007a). Google image labeler. Retrieved January 7, 2007, from images.google.com/imagelabeler
53. Google. (2007b). Google video U.K. Retrieved January 7, 2007, from video.google.co.uk
54. Google. (2007c). Picasa. Retrieved January 7, 2007, from picasa.google.com
55. Gordon, C. (1996). Patterns of user queries in an ICONCLASS database. Visual Resources, 12, 177-186.
56. Graves, A., & Lalmas, M. (2002). Video retrieval using an MPEG-7 based inference network. Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 339-346.
57. Greisdorf, H., & O'Connor, B. (2002). Modelling what users see when they look at images: A cognitive viewpoint. Journal of Documentation, 58(1), 6-29.
58. Halliwell, L. (2005). Halliwell's film, video & DVD guide 2006 (Ed. J. Walker). London: Harper Collins.
59. Hare, J. S., Lewis, P. H., Enser, P. G. B., & Sandom, C. J. (2006a). A linear-algebraic technique with an application in semantic image retrieval. Image and Video Retrieval: Proceedings of the 5th International Conference (Lecture Notes in Computer Science, 4071), 31-40.
60. Hare, J. S., Lewis, P. H., Enser, P. G. B., & Sandom, C. J. (2006b). Mind the gap: Another look at the problem of the semantic gap in image retrieval. Proceedings of Multimedia Content Analysis, Management and Retrieval (SPIE vol. 6073) 607309-1-607309-12.
61. Harper, D. J., & Eakins, J. P. (Eds.). (1999). The challenge of image retrieval: Papers presented at CIR99-Second UK Conference on Image Retrieval. Newcastle upon Tyne, UK: University of Northumbria at Newcastle.
62. Harrison, H. W. (Ed.). (1991). The FIAF cataloguing rules for film archives. Munich, Germany: Saur.
63. Hartill, R. (2007). Plot summary for The Ladykillers (1955). Internet movie database. Retrieved January 7, 2007, from www.imdb.com/title/tt0048281/plot summary
64. Hastings, S. K. (1995). Query categories in a study of intellectual access to digitized art images. Proceedings of the 58th Annual Meeting of the American Society for Information Science, 3-8.
65. Hauptmann, A. G. (2005). Lessons for the future from a decade of Informedia video analysis research. Image and Video Retrieval: Proceedings of the Fourth International Conference (Lecture Notes in Computer Science, 3568), 1-10.
66. Hertzum, M. (2003). Requests for information from a film archive: A case study of multimedia retrieval. Journal of Documentation, 59(2), 168-186.
67. Hohl, L., Souvannavong, F., Merialdo, B., & Huet, B. (2004). Using structure for video object retrieval. Image and Video Retrieval: Proceedings of the Third International Conference (Lecture Notes in Computer Science, 3115), 564-572.
68. Hollink, L., Little, S., & Hunter, J. (2005). Evaluating the application of semantic inferencing rules to image annotation. Proceedings of the 3rd International Conference on Knowledge Capture, 91-98.
69. Hollink, L., Nguyen, G. P., Koelma, D. C., Schreiber, A. T., & Worring, M. (2005). Assessing user behaviour in news video retrieval. IEE Proceedings on Vision, Image and Signal Processing, 152(6), 911-918.
70. Hollink, L., Schreiber, A. T., Wielinga, B. J., & Worring, M. (2004). Classification of user image descriptions. International Journal of Human Computer Studies, 61 (5), 601-621.
71. Hollink, L., Schreiber, A. T., Wielemaker, J., & Wielinga, B. J. (2003). Semantic annotation of image collections. In S. Handschuh, M. Koivunen, R. Dieng, & S. Staab (Eds.), Knowledge Capture 2003: Proceedings Knowledge Markup and Semantic Annotation Workshop (pp. 41-48). New York: ACM.
72. Hu, B., Dasmahapatra, S., Lewis, P., & Shadbolt, N. (2003). Ontology-based medical image annotation with description logics. Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence, 77-82.
73. Infotrends/CAP Ventures. (2005). News. Retrieved January 7, 2007, from www.infotrends-rgi.com/home/Press/itPress/2005/1.11.05.html
74. International Council of Museums. (2007). The CIDOC conceptual reference model. Retrieved January 7, 2007, from cidoc.ics.forth.gr
75. Internet Movie Database Inc. (2007). The Internet movie database. Retrieved January 7, 2007, from www.imdb.com
76. J. Paul Getty Trust. (2000a). Art & architecture thesaurus online. Retrieved January 7, 2007, from www.getty.edu/research/ conducting_research/vocabularies/ aat
77. J. Paul Getty Trust. (2000b). Getty thesaurus of geographic names online. Retrieved January 7, 2007, from www.getty.edu/research/conducting_ research/vocabularies/tgn
78. J. Paul Getty Trust. (2000c). Union list of artist names online. Retrieved January 7, 2007, from www.getty.edu/research/conducting_research/ vocabularies/ulan
79. Jaimes, A., & Chang, S.-F. (2000). A conceptual framework for indexing visual information at multiple levels. Internet Imaging (SPIE proceedings vol. 3964). Retrieved January 14, 2007, from www.ee.columbia.edu/ ∼ajaimes/Pubs/spie00_internet.pdf
80. Jaimes, A., Christel, M., Gilles, S., Sarukkai, R., & Ma, W.-Y. (2005). Multimedia information retrieval: What is it, and why isn't anyone using it? Proceedings of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval, 3-8.
81. Joint Steering Committee for Revision of AACR. (2005). Anglo American cataloguing rules (2nd ed., 2002 Revision [with 2003, 2004, and 2005 updates]). Chicago: American Library Association.
82. Johansson, B. (2000). A survey on: Contents based search in image databases. Retrieved January 7, 2007, from www.cvl.isy.liu.se/ScOut/TechRep/ PaperInfo/bj2000.html
83. Jörgensen, C. (1998). Attributes of images in describing tasks: An investigation. Information Processing & Management, 34(2/3), 161-174.
84. Jörgensen, C. (1999). Access to pictorial material: A review of current research and future prospects. Computers and the Humanities, 33(4), 293-318.
85. Jörgensen, C. (2003). Image retrieval: Theory and research. Lanham, MD: Scarecrow Press.
86. Jörgensen, C., Jaimes, A., Benitez, A. B., & Chang, S.-F. (2001). A conceptual framework and empirical research for classifying visual descriptors. Journal of the American Society for Information Science, 52(11), 938-947.
87. Jörgensen, C., & Jörgensen, P. (2005). Image querying by image professionals. Journal of the American Society for Information Science and Technology, 56(12), 1346-1359.
88. Keister, L. H. (1994). User types and queries: Impact on image access systems. In R. Fidel, T. B. Hahn, E. M. Rasmussen, & P. J. Smith (Eds.), Challenges in indexing electronic text and images (pp. 7-22). Medford, NJ: Learned Information.
89. Kherfi, M. L., Ziou, D., & Bernardi, A. (2004). Image retrieval from the World Wide Web: Issues, techniques, and systems. ACM Computing Surveys, 36(1), 35-67.
90. Kim, S.-K., Hwang, D. S., Kim, J.-Y., & Seo, Y.-S. (2005). An effective news anchorperson shot detection method based on adaptive audio/visual model generation. Image and Video Retrieval: Proceedings of the Fourth International Conference (Lecture Notes in Computer Science, 3568), 276-285.
91. Krause, M. C. (1998). Intellectual problems of indexing picture collections. Audiovisual Librarian, 14(2), 73-81.
92. Li, J., & Wang, Z. (2003). Automatic linguistic indexing of pictures by a statistical modelling approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(9), 1075-1088.
93. Library of Congress. (2005). MARC standards. Retrieved January 7, 2007, from www.loc.gov/marc
94. Library of Congress. (2006). Thesaurus for graphic materials I: Subject terms. Retrieved January 7, 2007, from www.loc.gov/rr/print/tgm1
95. Liu, Y., Dellaert, F., & Rothfus, W. E. (1998). Classification driven semantic based medical image indexing and retrieval: A technical report of the Robotics Institute at Carnegie Mellon University (CMU-RI-TR-98-25). Pittsburgh, PA: The Institute.
96. Markey, K. (1984). Interindexer consistency tests: A literature review and report of a test of consistency in indexing visual materials. Library & Information Science Research, 6(2), 155-177.
97. Markkula, M., & Sormunen, E. (2000). End-user searching challenges indexing practices in the digital newspaper photo archive. Information Retrieval, 1(4), 259-285.
98. Martinez, J. M. (Ed.). (2004). MPEG-7 overview (version 10). Palma de Mallorca, Spain: International Organisation for Standardisation.
99. Mikhalenko, P. (2005). Introducing SKOS. Retrieved January 7, 2007, from www.xml.com/pub/a/2005/06/22/skos.html
100. Moënne-Loccoz, N., Bruno, E., & Marchand-Maillet, S. (2006). Local feature trajectories for efficient event-based indexing of video sequences. Image and Video Retrieval: Proceedings of the 5th International Conference (Lecture Notes in Computer Science, 4071), 82-91.
101. Moving Image Collections. (2006a). Cataloging and metadata portal. Retrieved January 7, 2007, from mic.imtc.gatech.edu/catalogers_portal/ cat_unicatlg.htm
102. Moving Image Collections. (2006b). Moving image collections. Retrieved January 7, 2007, from mic.loc.gov
103. Müller, H., Michoux, N., Bandon, D., & Geissbuhler, A. (2004). A review of content-based image retrieval systems in medical applications: Clinical benefits and future directions. International Journal of Medical Informatics, 73(1), 1-23.
104. Naphade, M. R., & Smith, J. R. (2004). On the detection of semantic concepts at TRECVID. Proceedings of the 12th annual ACM international conference on Multimedia, 660-667.
105. Optibase, Inc. (2007). Video archiving at CNN. Retrieved January 7, 2007, from www.optibase.com/content.aspx?id=395
106. Ornager, S. (1995). The newspaper image database: Empirical supported analysis of users' typology and word association clusters. Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 212-218.
107. Ozkan, D., & Duygulu, P. (2006). Finding people frequently appearing in news. Image and Video Retrieval: Proceedings of the 5th International Conference (Lecture Notes in Computer Science, 4071), 173-182.
108. Panofsky, E. (1962). Studies in iconology. New York: Harper & Row.
109. Petkoviç, M., & Jonker, W. (2004). Content-based video retrieval: A database perspective. Boston: Kluwer.
110. Pisciotta, H. (2005). Understanding the picture user. Advances in Librarianship, 29, 223-245.
111. Princeton University, Cognitive Science Laboratory. (2007). WordNet a lexical database for the English language. Retrieved January 7, 2007, from wordnet. princeton.edu
112. Rasmussen, E. M. (1997). Indexing images. Annual Review of Information Science and Technology, 32, 169-196.
113. Rijksbureau voor Kunsthistorische Documentatie. (2007). Iconclass home page. Retrieved January 7, 2007, from www.iconclass.nl
114. Ros, J., Laurent, C., & Lefebvre, G. (2006). A cascade of unsupervised and supervised neural networks. Image and Video Retrieval: Proceedings of the 5th International Conference (Lecture Notes in Computer Science, 4071), 92-101.
115. Rui, Y., & Huang, T. S. (2001). Relevance feedback techniques in image retrieval. In M. S. Lew (Ed.), Principles of visual information retrieval (pp. 219-258). New York: Springer.
116. Rui, Y., Huang, T. S., & Chang, S.-F. (1999). Image retrieval: Current techniques, promising directions, and open issues. Journal of Visual Communication and Image Representation, 10, 39-62.
117. Sandom, C. J., & Enser, P. G. B. (2002). VIRAMI: Visual information retrieval for archival moving imagery (Library and Information Commission Research Report 129). London: The Council for Museums, Archives and Libraries.
118. Santini, S., & Jain, R. C. (1997). Do images mean anything? Proceedings of IEEE International Conference on Image processing, 564-567.
119. Schreiber, G., Dubbeldam, B., Wielemaker, J., & Wielinga, B. (2001). Ontology-based photo annotation. IEEE Intelligent Systems, 16(3), 2-10.
120. Shatford, S. (1986). Analyzing the subject of a picture: A theoretical approach. Cataloging & Classification Quarterly, 5(3), 39-61.
121. Shatford Layne, S. (1994). Some issues in the indexing of images. Journal of the American Society for Information Science, 45(8), 583-588.
122. Smeaton, A. F. (2004). Indexing, browsing and searching of digital video. Annual Review of Information Science and Technology, 38, 371-407.
123. Smeaton, A. F. (2005). Large scale evaluations of multimedia information retrieval: The TRECVid experience. Image and Video Retrieval: Proceedings of the Fourth International Conference (Lecture Notes in Computer Science, 3568), 11-17.
124. Smeaton, A. F., O'Connor, N. E., & Lee, H. (2005). Físchlár-News: Multimedia access to broadcast TV news. ERCIM News, 62. Retrieved January 7, 2007, from www.ercim.org/publication/ Ercim_News/enw62/smeaton.html
125. Smeulders, A. W. M., Worring, M., Santini, S., Gupta, A., & Jain, R. C. (2000). Content-based retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12), 1349-1380.
126. State Hermitage Museum. (2003). Digital Collection: QBIC colour and layout searches. Retrieved January 7, 2007, from monarch.hermitage.ru/fcgi- bin/db2www/qbicSearch.mac/qbic?selLang=English
127. Sundaram, H., Naphade, M., Smith, J. R., & Yong, R. (Eds.). (2006). Image and Video Retrieval: Proceedings of the 5th International Conference (Lecture Notes in Computer Science, 4071).
128. Svenonius, E. (1994). Access to nonbook materials: The limits of subject indexing for visual and aural languages. Journal of the American Society for Information Science, 45(8), 600-606.
129. Technical Advisory Service for Images. (2007). Resources: Case studies of digitisation projects. Retrieved January 7, 2007, from www.tasi.ac.uk/resources/casestudies.html
130. Trant, J. (2004). Image retrieval benchmark database service: A needs assessment and preliminary development plan: A report prepared for the Council on Library and Information Resources and the Coalition for Networked Information. Retrieved January 7, 2007, from www.clir.org/PUBS/reports/ trant04.html
131. University of California at Los Angeles. (2007). UCLA film and television archive: About us. Retrieved January 7, 2007, from www.cinema.ucla.edu/about/ aboutus.html
132. University of Kent at Canterbury. (2007). Centre for the Study of Cartoons and Caricatures. Retrieved January 7, 2007, from library.kent.ac.uk/cartoons
133. University of Washington. (2007). Index of / research / imagedatabase / ground truth. Retrieved January 7, 2007, from www.cs.washington.edu/ research/ imagedatabase/groundtruth
134. Veltkamp, R. C., & Tanase, M. (2001). CBIR system survey. Retrieved January 7, 2007, from www.aa-lab.cs.uu.nl/cbirsurvey/cbir-survey/cbir- survey.html
135. Visual Resources Association. (2002). Core categories (version 3). Retrieved January 7, 2007 from www.vraweb.org/vracore3.htm
136. Whitbrock, M. J., & Hauptmann, A. G. (1998). Speech recognition for a digital video library. Journal of the American Society for Information Science, 49(7), 619-632.
137. White-Hensen, W. (2000). Archival moving image materials: A cataloging manual (2nd ed.). Washington, DC: Library of Congress, Cataloging Distribution Service.
138. Yahoo! Inc. (2007a). Flickr. Retrieved January 7, 2007, from www.flickr.com
139. Yahoo! Inc. (2007b). Yahoo! Video. Retrieved January 7, 2007, from video.yahoo.com
140. Yang, J., & Hauptmann, A. G. (2006). Annotating news video with locations. Image and Video Retrieval: Proceedings of the 5th International Conference (Lecture Notes in Computer Science, 4071), 153-162.
141. Yang, M., Wildemuth, B. M., & Marchionini, G. (2004). The relative effectiveness of concept-based versus content-based video retrieval. Proceedings of the 12th Annual ACM International Conference on Multimedia, 368-371.
142. YouTube Inc. (2007). YouTube. Retrieved January 7, 2007, from www.youtube.com
143. Zhao, R., & Grosky, W. I. (2002). Bridging the semantic gap in image retrieval. In T. K. Shih (Ed.), Distributed multimedia databases: Techniques and applications (pp. 14-36). Hershey, PA: Idea Group Publishing.