Enhancing Recognition of Visual Concepts with Primitive Color Histograms via Non-sparse Multiple Kernel Learning

  • Alexander Binder
  • Motoaki Kawanabe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6242)

Abstract

In order to achieve good performance in image annotation tasks, it is necessary to combine information from various image features. In recent competitions on photo annotation, many groups employed the bag-of-words (BoW) representations based on the SIFT descriptors over various color channels. In fact, it has been observed that adding other less informative features to the standard BoW degrades recognition performances. In this contribution, we will show that even primitive color histograms can enhance the standard classifiers in the ImageCLEF 2009 photo annotation task, if the feature weights are tuned optimally by non-sparse multiple kernel learning (MKL) proposed by Kloft et al.. Additionally, we will propose a sorting scheme of image subregions to deal with spatial variability within each visual concept.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)MATHGoogle Scholar
  2. 2.
    Müller, K.R., Mika, S., Rätsch, G., Tsuda, K., Schölkopf, B.: An introduction to kernel-based learning algorithms. IEEE Trans. Neural Netw. 12(2), 181–201 (2001)CrossRefGoogle Scholar
  3. 3.
    Kloft, M., Brefeld, U., Sonnenburg, S., Laskov, P., Müller, K.R., Zien, A.: Efficient and accurate Lp-norm multiple kernel learning. In: Adv. In: Neur. Inf. Proc. Sys., NIPS (2009)Google Scholar
  4. 4.
    Csurka, G., Bray, C., Dance, C., Fan, L.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV 2004, Prague, Czech Republic, pp. 1–22 (May 2004)Google Scholar
  5. 5.
    Nowak, S., Dunker, P.: Overview of the CLEF 2009 large-scale visual concept detection and annotation task. In: Peters, C., et al. (eds.) CLEF 2009 Workshop, Part II. LNCS, vol. 6242, pp. 94–109. Springer, Heidelberg (2010)Google Scholar
  6. 6.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comp. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  7. 7.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Proceedings of CVPR 2006, New York, USA, pp. 2169–2178 (2006)Google Scholar
  8. 8.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pat. Anal. & Mach. Intel. 27(10), 1615–1630 (2005)CrossRefGoogle Scholar
  9. 9.
    van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluating color descriptors for object and scene recognition. IEEE Trans. Pat. Anal. & Mach. Intel. (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Alexander Binder
    • 1
  • Motoaki Kawanabe
    • 1
    • 2
  1. 1.Fraunhofer Institute FIRSTBerlinGermany
  2. 2.TU BerlinBerlinGermany

Personalised recommendations