Skip to main content

Overview of the ImageCLEF 2007 Object Retrieval Task

  • Conference paper
Advances in Multilingual and Multimodal Information Retrieval (CLEF 2007)

Abstract

We describe the object retrieval task of ImageCLEF 2007, give an overview of the methods of the participating groups, and present and discuss the results.

The task was based on the widely used PASCAL object recognition data to train object recognition methods and on the IAPR TC-12 benchmark dataset from which images of objects of the ten different classes bicycles, buses, cars, motorbikes, cats, cows, dogs, horses, sheep, and persons had to be retrieved.

Seven international groups participated using a wide variety of methods. The results of the evaluation show that the task was very challenging and that different methods for relevance assessment can have a strong influence on the results of an evaluation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Everingham, M., et al.: The 2005 PASCAL Visual Object Classes Challenge. In: Quiñonero-Candela, J., Dagan, I., Magnini, B., d’Alché-Buc, F. (eds.) MLCW 2005. LNCS (LNAI), vol. 3944, pp. 117–176. Springer, Heidelberg (2006)

    Google Scholar 

  2. Everingham, M., Zisserman, A., Williams, C., Gool, L.V.: The Pascal Visual Object Classes Challenge 2006 (VOC2006) results. Technical report (2006), http://www.pascal-network.org/

  3. Clough, P.D., Müller, H., Sanderson, M.: Overview of the CLEF cross–language image retrieval track (ImageCLEF) 2004. In: Peters, C., Clough, P.D., Jones, G.J.F., Gonzalo, J., Kluck, M., Magnini, B. (eds.) Multilingual Information Access for Text, Speech and Images: Result of the fifth CLEF evaluation campaign, Bath, England. LNCS. Springer, Heidelberg (2005)

    Google Scholar 

  4. Moellic, P.A., Fluhr, C.: ImageEVAL 2006 official campaign. Technical report, ImagEVAL (2006)

    Google Scholar 

  5. Grubinger, M., Clough, P., Hanbury, A., Müller, H.: Overview of the ImageCLEF 2007 photographic retrieval task. In: Working Notes of the 2007 CLEF Workshop, Budapest, Hungary (2007)

    Google Scholar 

  6. Müller, H., Deselaers, T., Kim, E., Kalpathy-Cramer, J., Deserno, T.M., Clough, P., Hersh, W.: Overview of the ImageCLEFmed 2007 medical retrieval and annotation tasks. In: Working Notes of the 2007 CLEF Workshop, Budapest, Hungary (2007)

    Google Scholar 

  7. Grubinger, M., Clough, P., Müller, H., Deselaers, T.: The IAPR benchmark: A new evaluation resource for visual information systems. In: LREC 2006 OntoImage 2006: Language Resources for Content-Based Image Retrieval, Genoa, Italy (in press, 2006)

    Google Scholar 

  8. Braschler, M., Peters, C.: CLEF methodology and metrics. In: Peters, C., Braschler, M., Gonzalo, J., Kluck, M. (eds.) CLEF 2001. LNCS, vol. 2406, pp. 394–404. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  9. Deselaers, T., Hanbury, A., Viitaniemi, V., Benczúr, A., Brendel, M., Daróczy, B., Escalante Balderas, H.J., Gevers, T., Hernández Gracidas, C.A., Hoi, S.C.H., Laaksonen, J., Li, M., Marin Castro, H.M., Ney, H., Rui, X., Sebe, N., Stöttinger, J., Wu, L.: Overview of the ImageCLEF 2007 object retrieval task. In: Working notes of the CLEF 2007 Workshop, Budapest, Hungary (2007)

    Google Scholar 

  10. Viitaniemi, V., Laaksonen, J.: Thoughts on evaluation of image retrieval inspired by ImageCLEF 2007 object retrieval task. In: MUSCLE / ImageCLEF Workshop on Image and Video Retrieval Evaluation, Budapest, Hungary (2007)

    Google Scholar 

  11. Chen, Y., Wang, J.Z.: Image categorization by learning and reasoning with regions. Journal of Machine Learning Research 5, 913–939 (2004)

    Google Scholar 

  12. Prasad, B.G., Biswas, K.K., Gupta, S.K.: Region-based image retrieval using integrated color, shape, and location index. Computer Vision and Image Understanding 94(1-3), 193–233 (2004)

    Article  Google Scholar 

  13. Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobworld: Image segmentation using expectation-maximization and its application to image querying. PAMI 24(8), 1026–1038 (2002)

    Google Scholar 

  14. Lv, Q., Charikar, M., Li, K.: Image similarity search with compact data structures. In: CIKM 2004: Proceedings of the thirteenth ACM international conference on Information and knowledge management, pp. 208–217. ACM Press, New York (2004)

    Chapter  Google Scholar 

  15. Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. International Journal of Computer Vision 59 (2004)

    Google Scholar 

  16. Shi, J., Malik, J.: Normalized cuts and image segmentation. PAMI 22, 888–905 (2000)

    Google Scholar 

  17. Viitaniemi, V., Laaksonen, J.: Improving the accuracy of global feature fusion based image categorisation. In: Falcidieno, B., Spagnuolo, M., Avrithis, Y., Kompatsiaris, I., Buitelaar, P. (eds.) SAMT 2007. LNCS, vol. 4816, pp. 1–14. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  18. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm

  19. Laaksonen, J., Koskela, M., Oja, E.: PicSOM—Self-organizing image retrieval with MPEG-7 content descriptions. IEEE Transactions on Neural Networks, Special Issue on Intelligent Multimedia Processing 13(4), 841–853 (2002)

    Google Scholar 

  20. Escalante, H.J., y Gómez, M.M., Sucar, L.E.: Word co-occurrence and MRFs for improving automatic image annotation. In: Proceedings of the 18th British Machine Vision Conference (BMVC 2007), Warwick, UK (September, 2007)

    Google Scholar 

  21. Shi, J., Malik, J.: Normalized cuts and image segmentation. PAMI 22(8), 888–905 (2000)

    Google Scholar 

  22. Marin-Castro, H.M., Sucar, L.E., Morales, E.F.: Automatic image annotation using a semi-supervised ensemble of classifiers. In: 12th Iberoamerican Congress on Pattern Recognition CIARP 2007, Viña del Mar, Valparaiso, Chile. LNCS. Springer, Heidelberg (to appear, 2007)

    Google Scholar 

  23. Freund, Y., Schapire, R.: Experiments with a new boosting algorithm. In: International Conference on Machine Learning, pp. 148–156 (1996)

    Google Scholar 

  24. Wu, L., Li, M.J., Li, Z.W., Ma, W.Y., Yu, N.H.: Visual language modeling for image classification. In: 9th ACM SIGMM International Workshop on Multimedia Information Retrieval (MIR 2007), Augsburg, Germany (2007)

    Google Scholar 

  25. Fergus, R., Fei-Fei, L., Perona, P., Zisserman, A.: Learning object categories from google’s image search. In: International Conference on Computer Vision, Beijing, China (2005)

    Google Scholar 

  26. Hoi, S.C.H., Lyu, M.R.: A novel log-based relevance feedback technique in content-based image retrieval. In: 12th ACM International Conference on Multimedia (MM 2004), New York, NY, USA, pp. 24–31 (2004)

    Google Scholar 

  27. Hoi, S.C., Lyu, M.R., Jin, R.: A unified log-based relevance feedback scheme for image retrieval. IEEE Transactions on Knowledge and Data Engineering 18(4), 509–524 (2006)

    Article  Google Scholar 

  28. Harris, C., Stephens, M.: A combined corner and edge detection. In: 4th Alvey Vision Conference, pp. 147–151 (1988)

    Google Scholar 

  29. Montesinos, P., Gouet, V., Deriche, R.: Differential invariants for color images. In: ICPR, p. 838 (1998)

    Google Scholar 

  30. van de Weijer, J., Gevers, T.: Edge and corner detection by photometric quasi-invariants. PAMI 27(4), 625–630 (2005)

    Google Scholar 

  31. van de Weijer, J., Gevers, T., Bagdanov, A.: Boosting color saliency in image feature detection. PAMI 28(1), 150–156 (2006)

    Google Scholar 

  32. Stöttinger, J., Hanbury, A., Sebe, N., Gevers, T.: Do colour interest points improve image retrieval? In: ICIP (to appear, 2007)

    Google Scholar 

  33. Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)

    Article  Google Scholar 

  34. Mikolaczyk, K., Schmid, C.: A performance evaluation of local descriptors. PAMI 27(10), 1615–1630 (2005)

    Google Scholar 

  35. Zhang, J., Marszalek, M., Lazebnik, S., Schmid, C.: Local features and kernels for classification of texture and object categories: A comprehensive study. CWPR 73(2), 213–238 (2006)

    Google Scholar 

  36. Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. IJCV 40(2), 99–121 (2000)

    Article  MATH  Google Scholar 

  37. Deselaers, T., Keysers, D., Ney, H.: Discriminative training for object recognition using image patches. In: IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, vol. 2, pp. 157–162 (2005)

    Google Scholar 

  38. Deselaers, T., Keysers, D., Ney, H.: Improving a discriminative approach to object recognition using image patches. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds.) DAGM 2005. LNCS, vol. 3663, pp. 326–333. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Carol Peters Valentin Jijkoun Thomas Mandl Henning Müller Douglas W. Oard Anselmo Peñas Vivien Petras Diana Santos

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Deselaers, T. et al. (2008). Overview of the ImageCLEF 2007 Object Retrieval Task. In: Peters, C., et al. Advances in Multilingual and Multimodal Information Retrieval. CLEF 2007. Lecture Notes in Computer Science, vol 5152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85760-0_58

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-85760-0_58

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85759-4

  • Online ISBN: 978-3-540-85760-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics