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Can an intermediary collection help users search image databases without annotations?

Published: 21 June 2010 Publication History

Abstract

Developing methods for searching image databases is a challenging and ongoing area of research. A common approach is to use manual annotations, although generating annotations can be expensive in terms of time and money, and therefore may not be justified in many situations. Content-based search techniques which extract visual features from image data can be used, but users are typically forced to express their information need using example images, or through sketching interfaces. This can be difficult if no visual example of the information need is available, or when the information need cannot be easily drawn.
In this paper, we consider an alternative approach which allows a user to search for images through an intermediate database. In this approach, a user can search using text in the intermediate database as a way of finding visual examples of their information need. The visual examples can then be used to search a database that lacks annotations. Three experiments are presented which investigate this process. The first experiment automatically selects the image queries from the intermediary database; the second instead uses images which have been hand-picked by users. A third experiment, an interactive study, is then presented this study compares the intermediary interface to text search, where we consider text as an upper bound of performance. For this last study, an interface which supports the intermediary search process is described. Results show that while performance does not match manual annotations, users are able to find relevant material without requiring collection annotations.

References

[1]
Chan, Y., and Kung, S. Y. 1997. A hierarchical algorithm for image retrieval by sketch. In IEEE First Workshop on Multimedia Signal Processing, (June, 1997), 564-56.
[2]
Chun, S., Cherry, R., Hiwiller, D., Trant, J. and Wyman, B. 2006. Steve.museum: An Ongoing Experiment in Social Tagging, Folksonomy, and Museums, In Museums and the Web 2006, Albuquerque, US (March 2006.
[3]
Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D., Steele, D., and Yanker, P. 1997, Query by Image and Video Content: The QBIC System, Intelligent Multimedia Information Retrieval, In Intelligent Multimedia Information Retrieval, M. T. Maybury, Ed. MIT Press, Cambridge, MA, 7--22.
[4]
Grubinger, M. 2007. Analysis and Evaluation of Visual Information Systems Performance. Doctoral Thesis, Victoria University, Melbourne, Australi.
[5]
Grubinger, M. And Clough, P. 2007. On the Creation of Query Topics for ImageCLEFphoto. In Proceedings of the Third Workshop on Image and Video Retrieval Evaluation, Budapest, Hungary (September 2007) 50--6.
[6]
Guy, M. and Tonkin, E. 2006. Folksonomies: Tidying Up Tags?, D-Lib Magazine, 12, 1 (January 2006.
[7]
Kraaij, W. and Awad, G. 2008. TRECVID 2008 High-Level Feature Task: Overview. In Proceedings of the TRECVID 2008 workshop, Gaithersburg, MD, USA, (November 2008.
[8]
LSCOM Lexicon Definitions and Annotations Version 1.0. 2006. In DTO Challenge Workshop on Large Scale Concept Ontology for Multimedia, Columbia University ADVENT Technical Report #217-2006-3, (March 2006.
[9]
Manjunath, B. S., Salembier, P., and Sikora, T. (eds) 2002. Introduction to MPEG-7: Multimedia Content Description Interface, John Wiley & Sons Lt.
[10]
Naphade, M. R., Kennedy, L., Kender, J. R., Chang, S.-F., Smith, J. R., Over, P. and Hauptmann, A. 2005. A Light Scale Concept Ontology for Multimedia Understanding for TRECVID 2005, IBM Research Technical Report, Computer Science, RC23612, W0505-104, (May 17.
[11]
1Over, P., Awad, G., Kraaij, W., and Smeaton., A. F. 2007. Trecvid 2007 overview. In Proceedings of the TRECVID 2007 Workshop, Gaithersburg, MD, USA, (November 2007.
[12]
Setz, A. T. and Snoek, C. G. 2009. Can social tagged images aid concept-based video search? In Proceedings of ICME 2009, New York, NY (July 2009.
[13]
Smeulders, A. W., Worring, M., Santini, S., Gupta, A., and Jain, R. 2000. Content-Based Image Retrieval at the End of the Early Years. IEEE Trans. Pattern Anal. Mach. Intell. 22, 12 (Dec. 2000), 1349--138.
[14]
Snoek, C. G., Worring, M., van Gemert, J. C., Geusebroek, J., and Smeulders, A. W. 2006. The challenge problem for automated detection of 101 semantic concepts in multimedia. In Proceedings of the 14th Annual ACM international Conference on Multimedia. MULTIMEDIA '06. ACM, New York, NY, 421--43.
[15]
Tait, J., McDonald, S., and Lai, T. 2001. CHROMA: An Experimental Image Retrieval System. In Proceedings of the 1st international Workshop on New Developments in Digital Libraries (July, 2001). P. T. Isaías, Ed. ICEIS Press, 141--151.
[16]
Urruty, T., Belkouch, F., and Djeraba, C. 2006. Efficient Indexing for High Dimensional Data: Applications to a Video Search Tool. In 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM KDD 2006), Philadelphia, USA (August 2006.
[17]
Urban, J. and Jose, J. M. 2006. EGO: A personalized multimedia management and retrieval tool. Int. J. Intell. Syst. 21, 7 (July 2006), 725--74.
[18]
Urban, J., Hilaire, X., Hopfgartner, F., Villa, R., Jose, J.M., Chantamunee, S., and Gotoh, Y. 2006. Glasgow University at TRECVID 2006, In Proceedings of the TRECVID 2006 Workshop, Gaithersburg, MD, USA, (November 2006.
[19]
von Ahn, L. and Dabbish, L. 2004. Labeling Images with a Computer Game. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Vienna, Austria, April 24 - 29, 2004), ACM, New York, NY, 319--32.
[20]
von Ahn, L., Liu, R., and Blum, M. 2006. Peekaboom: a game for locating objects in images. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Montréal, Québec, Canada, April 22 - 27, 2006). R. Grinter, T. Rodden, P. Aoki, E. Cutrell, R. Jeffries, and G. Olson, Eds. CHI '06. ACM, New York, NY, 55--6.
[21]
2Dunlop, M. D. 1991. Multimedia Information Retrieval, PhD Thesis, Glasgow University Computing Science Research Report 1991/ R21, (October 1991.
[22]
2Hauptmann, A. G., Christel, M. G., and Yan, R. 2008. Video Retrieval based on Semantic Concepts, In Proceedings of the IEEE, 96, 4, 602--62.
[23]
Duygulu, P., Barnard, K., de Freitas, J. F. G., and Forsyth, D. A. 2002. Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. In Proceedings of the 7th European Conference on Computer Vision, ECCV'02, London, UK, Springer-Verlag, 97--112.

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    cover image ACM Conferences
    JCDL '10: Proceedings of the 10th annual joint conference on Digital libraries
    June 2010
    424 pages
    ISBN:9781450300858
    DOI:10.1145/1816123
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    Published: 21 June 2010

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    Author Tags

    1. content-based image retrieval
    2. search strategies

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    JCDL10: Joint Conference on Digital Libraries
    June 21 - 25, 2010
    Queensland, Gold Coast, Australia

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    • (2012)COPEProceedings of the 26th Annual BCS Interaction Specialist Group Conference on People and Computers10.5555/2377916.2377918(1-10)Online publication date: 10-Sep-2012
    • (2012)PhotoFallProceedings of the 21st ACM international conference on Information and knowledge management10.1145/2396761.2398695(2575-2578)Online publication date: 29-Oct-2012
    • (2012)Collecting relevance feedback on titles and photographs in weblog postsProceedings of the 2012 ACM international conference on Intelligent User Interfaces10.1145/2166966.2166993(139-148)Online publication date: 14-Feb-2012

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