Spatio-chromatic Opponent Features

  • Ioannis Alexiou
  • Anil A. Bharath
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8693)


This work proposes colour opponent features that are based on low-level models of mammalian colour visual processing. A key step is the construction of opponent spatio-chromatic feature maps by filtering colour planes with Gaussians of unequal spreads. Weighted combination of these planes yields a spatial center-surround effect across chromatic channels. The resulting feature spaces – substantially different to CIELAB and other colour-opponent spaces obtained by colour-plane differencing – are further processed to assign local spatial orientations. The nature of the initial spatio-chromatic processing requires a customised approach to generating gradient-like fields, which is also described. The resulting direction-encoding responses are then pooled to form compact descriptors. The individual performance of the new descriptors was found to be substantially higher than those arising from spatial processing of standard opponent colour spaces, and these are the first chromatic descriptors that appear to achieve such performance levels individually. For all stages, parametrisations are suggested that allow successful optimisation using categorization performance as an objective. Classification benchmarks on Pascal VOC 2007 and Bird-200-2011 are presented to show the merits of these new features.


Colour descriptors image categorization colour-opponency biologically-inspired pooling Bird 200 Pascal VOC 


  1. 1.
    Abdel-Hakim, A.E., Farag, A.A.: CSIFT: A SIFT descriptor with color invariant characteristics. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1978–1983 (2006)Google Scholar
  2. 2.
    Bosch, A., Zisserman, A., Muoz, X.: Scene classification using a hybrid generative/discriminative approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(4), 712–727 (2008)CrossRefGoogle Scholar
  3. 3.
    Branson, S., Wah, C., Schroff, F., Babenko, B., Welinder, P., Perona, P., Belongie, S.: Visual recognition with humans in the loop. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 438–451. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Brown, M., Hua, G., Winder, S.: Discriminative learning of local image descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(1), 43–57 (2011)CrossRefGoogle Scholar
  5. 5.
    Brown, M., Süsstrunk, S.: Multispectral SIFT for scene category recognition. In: Computer Vision and Pattern Recognition (CVPR), Colorado Springs, pp. 177–184 (June 2011)Google Scholar
  6. 6.
    Brown, M., Süsstrunk, S., Fua, P.: Spatio-chromatic decorrelation by shift invariant filtering. In: CVPR Workshop on Biologically Consistent Vision (WBCV 2011), Colorado Springs, pp. 9–16 (June 2011)Google Scholar
  7. 7.
    Buzás, P., Kóbor, P., Petykó, Z., Telkes, I., Martin, P.R., Lénárd, L.: Receptive field properties of color opponent neurons in the cat lateral geniculate nucleus. The Journal of Neuroscience 33(4), 1451–1461 (2013)CrossRefGoogle Scholar
  8. 8.
    Chai, Y., Rahtu, E., Lempitsky, V., Van Gool, L., Zisserman, A.: TriCoS: A tri-level class-discriminative co-segmentation method for image classification. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 794–807. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Chatfield, K., Lempitsky, V., Vedaldi, A., Zisserman, A.: The devil is in the details: an evaluation of recent feature encoding methods. In: Proceedings of the British Machine Vision Conference, BMVC (2011)Google Scholar
  10. 10.
    Everingham, M., Gool, L., Williams, C.I., Winn, J., Zisserman, A.: The Pascal Visual Object Classes (VOC) challenge. International Journal of Computer Vision 88(2), 303–338 (2010)CrossRefGoogle Scholar
  11. 11.
    Gao, S., Yang, K., Li, C., Li, Y.: A color constancy model with double-opponency mechanisms. In: IEEE International Conference on Computer Vision (ICCV), pp. 929–936. IEEE (2013)Google Scholar
  12. 12.
    Geusebroek, J.-M., Van den Boomgaard, R., Smeulders, A.W.M., Geerts, H.: Color invariance. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(12), 1338–1350 (2001)CrossRefGoogle Scholar
  13. 13.
    Johnson, E.N., Hawken, M.J., Shapley, R.: The orientation selectivity of color-responsive neurons in macaque V1. The Journal of Neuroscience 28(32), 8096–8106 (2008), doi:10.1523/JNEUROSCI.1404-08.2008CrossRefGoogle Scholar
  14. 14.
    Khan, R., Van de Weijer, J., Khan, F.S., Muselet, D., Ducottet, C., Barat, C.: Discriminative color descriptors. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2866–2873. IEEE (2013)Google Scholar
  15. 15.
    Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Review 51(3), 455–500 (2009)CrossRefzbMATHMathSciNetGoogle Scholar
  16. 16.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  17. 17.
    Sánchez, J., Perronnin, F., Mensink, T., Verbeek, J.: Image classification with the Fisher vector: Theory and practice. International Journal of Computer Vision 105(3), 222–245 (2013)CrossRefzbMATHMathSciNetGoogle Scholar
  18. 18.
    Van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluating color descriptors for object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9), 1582–1596 (2010)CrossRefGoogle Scholar
  19. 19.
    Simonyan, K., Vedaldi, A., Zisserman, A.: Descriptor learning using convex optimisation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 243–256. Springer, Heidelberg (2012), doi:10.1007/978-3-642-33718-5-18CrossRefGoogle Scholar
  20. 20.
    van de Weijer, J., Gevers, T., Bagdanov, A.D.: Boosting color saliency in image feature detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(1), 150–156 (2006)CrossRefGoogle Scholar
  21. 21.
    Yang, K., Gao, S., Li, C., Li, Y.: Efficient color boundary detection with color-opponent mechanisms. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2013)Google Scholar
  22. 22.
    Zhang, J., Barhomi, Y., Serre, T.: A new biologically inspired color image descriptor. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 312–324. Springer, Heidelberg (2012), doi:10.1007/978-3-642-33715-4-23CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ioannis Alexiou
    • 1
  • Anil A. Bharath
    • 1
  1. 1.BICV GroupImperial College LondonUK

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