Region-Based Annotation of Digital Photographs

  • Claudio Cusano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6626)

Abstract

We propose a region-based method for the annotation of outdoor photographs. First, images are oversegmented using the normalized cut algorithm. Each resulting region is described by color and texture features, and is then classified by a multi-class Support Vector Machine into seven classes: sky, vegetation, snow, water, ground, street, and sand. Finally, a rejection option is applied to discard those regions for which the classifier is not confident enough. For training and evaluation we used more than 12,000 images taken from the LabelMe project.

Keywords

Support Vector Machine Input Image Local Binary Pattern Image Annotation Automatic Image Annotation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References

  1. 1.
    Boutell, M., Luo, J., Shen, X., Brown, C.: Learning multi-label scene classification. Pattern Recognition 37(9), 1757–1771 (2004)CrossRefGoogle Scholar
  2. 2.
    Cheng, H., Wang, R.: Semantic modeling of natural scenes based on contextual Bayesian networks. Pattern Recognition 43(12), 4042–4054 (2010)CrossRefMATHGoogle Scholar
  3. 3.
    Ciocca, G., Cusano, C., Gasparini, F., Schettini, R.: Content aware image enhancement. In: Basili, R., Pazienza, M.T. (eds.) AI*IA 2007. LNCS (LNAI), vol. 4733, pp. 686–697. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Cooper, T.: Color segmentation as an aid to white balancing for digital still cameras, 4300, 164–171 (2000)Google Scholar
  5. 5.
    Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20, 273–297 (1995)MATHGoogle Scholar
  6. 6.
    Cusano, C., Ciocca, G., Schettini, R.: Image annotation using SVM. In: Proc. of Internet Imaging V. SPIE, vol. 5304, pp. 330–338 (2004)Google Scholar
  7. 7.
    Cusano, C., Gasparini, F., Schettini, R.: Image annotation for adaptive enhancement of uncalibrated color images. In: Bres, S., Laurini, R. (eds.) VISUAL 2005. LNCS, vol. 3736, pp. 216–225. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Fredembach, C., Estrada, F., Süsstrunk, S.: Memory colour segmentation and classification using class-specific eigenregions. Journal of the Society for Information Display 17(11), 921–931 (2009)CrossRefGoogle Scholar
  9. 9.
    Gasparini, F., Schettini, R.: Color balancing of digital photos using simple image statistics. Pattern Recognition 37(6), 1201–1217 (2004)CrossRefGoogle Scholar
  10. 10.
    Gijsenij, A., Gevers, T.: Color constancy using image regions. In: IEEE International Conference on Image Processing, vol. 3, pp. 501–504 (2007) Google Scholar
  11. 11.
    Guillaumin, M., Mensink, T., Verbeek, J., Schmid, C.: Tagprop: Discriminative metric learning in nearest neighbor models for image auto-annotation. In: IEEE 12th International Conference on Computer Vision, pp. 309–316 (2010) Google Scholar
  12. 12.
    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 Informaion Retrieval, pp. 119–126 (2003)Google Scholar
  13. 13.
    Malik, J., Belongie, S., Leung, T., Shi, J.: Contour and texture analysis for image segmentation. International Journal of Computer Vision 43, 7–27 (2001)CrossRefMATHGoogle Scholar
  14. 14.
    Millet, C., Bloch, I., Hede, P., Moellic, P.: Using relative spatial relationships to improve individual region recognition. In: European Workshop on the Integration of Knowledge, Semantics and Digital Media Technologies, EWIMT, vol. 5, pp. 119–126 (2005) Google Scholar
  15. 15.
    Ojala, T., Pietikäainen, M., Mäaenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)CrossRefGoogle Scholar
  16. 16.
    Rui, X., Li, M., Li, Z., Ma, W., Yu, N.: Bipartite graph reinforcement model for web image annotation. In: Proceedings of the 15th International Conference on Multimedia, pp. 585–594 (2007) Google Scholar
  17. 17.
    Russell, B., Torralba, A., Murphy, K., Freeman, W.: LabelMe: a database and webbased tool for image annotation. International Journal of Computer Vision 77(1), 157–173 (2008)CrossRefGoogle Scholar
  18. 18.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)CrossRefGoogle Scholar
  19. 19.
    Tsai, C., Hung, C.: Automatically annotating images with keywords: A review of image annotation systems. Recent Patents on Computer Science 1(1), 55–68 (2008)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Wang, C., Jing, F., Zhang, L., Zhang, H.: Content-based image annotation refinement. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)Google Scholar
  21. 21.
    Van de Weijer, J., Gevers, T., Bagdanov, A.: Boosting color saliency in image feature detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(1), 150–156 (2006)CrossRefGoogle Scholar
  22. 22.
    Wu, T., Lin, C., Weng, R.: Probability estimates for multi-class classification by pairwise coupling. The Journal of Machine Learning Research 5, 975–1005 (2004)MathSciNetMATHGoogle Scholar
  23. 23.
    Yuan, J., Li, J., Zhang, B.: Exploiting spatial context constraints for automatic image region annotation. In: Proceedings of the 15th International Conference on Multimedia, pp. 595–604 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Claudio Cusano
    • 1
  1. 1.DISCo (Dipartimento di Informatica, Sistemistica e Comunicazione)Università degli Studi di Milano-BicoccaMilanoItaly

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