Abstract
This paper regards images with captions as a cross-media parallel corpus, and presents a corpus-based relevance feedback approach to combine the results of visual and textual runs. Experimental results show that this approach performs well. Comparing with the mean average precision (MAP) of the initial visual retrieval, the MAP is increased from 8.29% to 34.25% after relevance feedback from cross-media parallel corpus. The MAP of cross-lingual image retrieval is increased from 23.99% to 39.77% if combining the results of textual run and visual run with relevance feedback. Besides, the monolingual experiments also show the consistent effects of this approach. The MAP of monolingual retrieval is improved from 39.52% to 50.53% when merging the results of the text and image queries.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Goodrum, A.A.: Image Information Retrieval: An Overview of Current Research. Information Science 3(2), 63–66 (2000)
Eidenberger, H., Breiteneder, C.: Semantic Feature Layers in Content-based Image Retrieval: Implementation of Human World Features. In: Proceedings of International Conference on Control, Automation, Robotic and Vision (2002)
Besançon, R., Hède, P., Moellic, P.A., Fluhr, C.: Cross-Media Feedback Strategies: Merging Text and Image Information to Improve Image Retrieval. In: Peters, C., Clough, P., Gonzalo, J., Jones, G.J.F., Kluck, M., Magnini, B. (eds.) CLEF 2004. LNCS, vol. 3491, pp. 709–717. Springer, Heidelberg (2005)
Jones, G.J.F., Groves, D., Khasin, A., Lam-Adesina, A., Mellebeek, B., Way, A.: Dublin City University at CLEF 2004: Experiments with the ImageCLEF St. Andrew’s Collection. In: Peters, C., Clough, P., Gonzalo, J., Jones, G.J.F., Kluck, M., Magnini, B. (eds.) CLEF 2004. LNCS, vol. 3491, pp. 653–663. Springer, Heidelberg (2005)
Baan, J., van Ballegooij, A., Geusenbroek, J.M., den Hartog, J., Hiemstra, D., List, J., Patras, I., Raaijmakers, S., Snoek, C., Todoran, L., Vendrig, J., de Vries, A., Westerveld, T., Worring, M.: Lazy Users and Automatic Video Retrieval Tools in the Lowlands. In: Proceedings of the Tenth Text REtrieval Conference, pp. 159–168 (2002)
Mori, Y., Takahashi, H., Oka, R.: Image-to-Word Transformation Based on Dividing and Vector Quantizing Images with Words. In: Proceedings of the First International Workshop on Multimedia Intelligent Storage and Retrieval Management (1999)
Duygulu, P., Barnard, K., Freitas, N., Forsyth, D.: Object Recognition as Machine Transla-tion: Learning a Lexicon for a Fixed Image Vocabulary. In: Proceedings of Seventh European Conference on Computer Vision, vol. 4, pp. 97–112 (2002)
Shi, J., Malik, J.: Normalized Cuts and Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society, Series B 39(1), 1–38 (1977)
Jeon, J., Lavrenko, V., Manmatha, R.: Automatic Image Annotation and Retrieval using Cross-Media Relevance Models. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 119–126 (2003)
Lavrenko, V., Manmatha, R., Jeon, J.: A Model for Learning the Semantics of Pictures. In: Proceedings of the Seventeenth Annual Conference on Neural Information Processing Systems (2003)
Lin, W.C., Chang, Y.C., Chen, H.H.: Integrating Textual and Visual Information for Cross-Language Image Retrieval. In: Lee, G.G., Yamada, A., Meng, H., Myaeng, S.-H. (eds.) AIRS 2005. LNCS, vol. 3689, pp. 454–466. Springer, Heidelberg (2005)
Squire, D.M., Müller, W., Müller, H., Raki, J.: Content-Based Query of Image Databases, Inspirations from Text Retrieval: Inverted Files, Frequency-Based Weights and Relevance Feedback. In: Scandinavian Conference on Image Analysis, pp. 143–149 (1999)
Robertson, S.E., Walker, S., Beaulieu, M.: Okapi at TREC-7: Automatic Ad Hoc, Filtering, VLC and Interactive. In: Proceedings of the Seventh Text REtrieval Conference, National Institute of Standards and Technology, pp. 253–264 (1998)
Lin, W.C., Chang, Y.C., Chen, H.H.: From Text to Image: Generating Visual Query for Image Retrieval. In: Peters, C., Clough, P., Gonzalo, J., Jones, G.J.F., Kluck, M., Magnini, B. (eds.) CLEF 2004. LNCS, vol. 3491, pp. 664–675. Springer, Heidelberg (2005)
Clough, P., Müller, H., Deselaers, T., Grubinger, M., Lehmann, T.M., Jensen, J., Hersh, W.: The CLEF 2005 Cross–Language Image Retrieval Track. In: Peters, C., Gey, F.C., Gonzalo, J., Müller, H., Jones, G.J.F., Kluck, M., Magnini, B., de Rijke, M., Giampiccolo, D. (eds.) CLEF 2005. LNCS, vol. 4022, pp. 535–557. Springer, Heidelberg (2006)
Feng, K.M., Chen, H.H.: Effects of Positive and Negative Information Needs on Information Retrieval. Bulletin of the College of Engineering, National Taiwan University 90, 35–42 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chang, YC., Lin, WC., Chen, HH. (2006). A Corpus-Based Relevance Feedback Approach to Cross-Language Image Retrieval. In: Peters, C., et al. Accessing Multilingual Information Repositories. CLEF 2005. Lecture Notes in Computer Science, vol 4022. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11878773_66
Download citation
DOI: https://doi.org/10.1007/11878773_66
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45697-1
Online ISBN: 978-3-540-45700-8
eBook Packages: Computer ScienceComputer Science (R0)