Advertisement

Oriented Shape Index Histograms for Cell Classification

  • Anders Boesen Lindbo LarsenEmail author
  • Anders Bjorholm Dahl
  • Rasmus Larsen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9127)

Abstract

We propose a novel extension to the shape index histogram feature descriptor where the orientation of the second-order curvature is included in the histograms. The orientation of the shape index is reminiscent but not equal to gradient orientation which is widely used for feature description. We evaluate our new feature descriptor using a public dataset consisting of HEp-2 cell images from indirect immunoflourescence lighting. Our results show that we can improve classification performance significantly when including the shape index orientation. Notably, we show that shape index orientation outperforms the gradient orientation on the dataset.

Keywords

Local Binary Pattern Feature Descriptor Shape Index Gradient Orientation Dominant Orientation 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Arandjelovic, R., Zisserman, A.: Three things everyone should know to improve object retrieval. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2911–2918 (2012)Google Scholar
  2. 2.
    Crosier, M., Griffin, L.: Using basic image features for texture classification. International Journal of Computer Vision 88(3), 447–460 (2010)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2005, vol. 1, pp. 886–893 (2005)Google Scholar
  4. 4.
    Foggia, P., Percannella, G., Soda, P., Vento, M.: Benchmarking HEp-2 cells classification methods. IEEE Transactions on Medical Imaging PP(99), 1–1 (2013)Google Scholar
  5. 5.
    Foggia, P., Percannella, G., Saggese, A., Vento, M.: Pattern recognition in stained HEp-2 cells: Where are we now? Pattern Recognition 47(7), 2305–2314 (2014)CrossRefGoogle Scholar
  6. 6.
    Koenderink, J.J., van Doorn, A.J.: Surface shape and curvature scales. Image and Vision Computing 10(8), 557–564 (1992)CrossRefGoogle Scholar
  7. 7.
    Larsen, A.B.L., Vestergaard, J.S., Larsen, R.: HEp-2 cell classification using shape index histograms with donut-shaped spatial pooling. IEEE Transactions on Medical Imaging 33(7), 1573–1580 (2014)CrossRefGoogle Scholar
  8. 8.
    Lazebnik, S., Schmid, C., Ponce, J.: A sparse texture representation using local affine regions. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(8), 1265–1278 (2005)CrossRefGoogle Scholar
  9. 9.
    Lindeberg, T.: Scale-space theory in computer vision. Springer (1993)Google Scholar
  10. 10.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  11. 11.
    Nosaka, R., Ohkawa, Y., Fukui, K.: Feature extraction based on co-occurrence of adjacent local binary patterns. In: Ho, Y.-S. (ed.) PSIVT 2011, Part II. LNCS, vol. 7088, pp. 82–91. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  12. 12.
    Ojala, T., Pietikainen, M., Maenpaa, 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
  13. 13.
    Pedersen, K., Stensbo-Smidt, K., Zirm, A., Igel, C.: Shape index descriptors applied to texture-based galaxy analysis. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 2440–2447 (December 2013)Google Scholar
  14. 14.
    Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Pereira, F., Burges, C., Bottou, L., Weinberger, K. (eds.) Advances in Neural Information Processing Systems, vol. 25, pp. 2951–2959. Curran Associates, Inc. (2012)Google Scholar
  15. 15.
    Tola, E., Lepetit, V., Fua, P.: Daisy: An efficient dense descriptor applied to wide-baseline stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(5), 815–830 (2010)CrossRefGoogle Scholar
  16. 16.
    Wiliem, A., Sanderson, C., Wong, Y., Hobson, P., Minchin, R.F., Lovell, B.C.: Automatic classification of human epithelial type 2 cell indirect immunofluorescence images using cell pyramid matching. Pattern Recognition (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Anders Boesen Lindbo Larsen
    • 1
    Email author
  • Anders Bjorholm Dahl
    • 1
  • Rasmus Larsen
    • 1
  1. 1.Department of Applied Mathematics and Computer ScienceTechnical University of DenmarkKongens LyngbyDenmark

Personalised recommendations