Continuous Multi-way Shape Measure for Dissimilarity Representation

  • Diana Porro-Muñoz
  • Robert P. W. Duin
  • Mauricio Orozco-Alzate
  • Isneri Talavera Bustamante
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)

Abstract

For many applications, a straightforward representation of objects is by multi-dimensional arrays e.g. signals. However, there are only a few classification tools which make a proper use of this complex structure to obtain a better discrimination between classes. Moreover, they do not take into account context information that can also be very beneficial in the classification process. Such is the case of multi-dimensional continuous data, where there is a connectivity between the points in all directions, a particular (differentiating) shape in the surface of each class of objects. The dissimilarity representation has been recently proposed as a tool for the classification of multi-way data, such that the multi-dimensional structure of objects can be considered in their dissimilarities. In this paper, we introduce a dissimilarity measure for continuous multi-way data and a new kernel for gradient computation. It allows taking the connectivity between the measurement points into account, using the information on how the shape of the surface varies in all directions. Experiments show the suitability of this measure for classifying continuous multi-way data.

Keywords

Classification Continuous multi-way data Dissimilarity representation Object representation 

References

  1. 1.
    Smilde, A.K., Bro, R., Geladi, P.: Multi-way Analysis. In: Applications in the Chemical Sciences. Wiley Publisher, England (2004)Google Scholar
  2. 2.
    Acar, E., Aykut-Bingol, C., Bingol, H., Bro, R., Yener, B.: Multiway analysis of epilepsy tensors. Bioinformatics 23, i10–i18 (2007)Google Scholar
  3. 3.
    Durante, C., Bro, R., Cocchi, M.: A classification tool for N-way array based on SIMCA methodology. Chem. and Intell. Lab. Syst. 106, 73–85 (2011)CrossRefGoogle Scholar
  4. 4.
    Porro-Muñoz, D., Duin, R.P.W., Talavera, I., Orozco-Alzate, M.: Classification of three-way data by the dissimilarity representation. Signal Processing 91(11), 2520–2529 (2011)CrossRefGoogle Scholar
  5. 5.
    Pekalska, E., Duin, R.P.W.: The Dissimilarity Representation For Pattern Recognition. Foundations and Applications. World Scientific, Singapore (2005)MATHGoogle Scholar
  6. 6.
    Zuo, W., Zhang, D., Wang, K.: An assembled matrix distance metric for 2DPCA-based image recognition. Pattern Recognition Letters 27, 210–216 (2006)CrossRefGoogle Scholar
  7. 7.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall, Inc., Upper Saddle River (2006)Google Scholar
  8. 8.
    Lathauwer, L., De Moor, B.: From matrix to tensor: Multilinear algebra and signal processing. In: Proc. 4th Int’l Conf. on Mathematics in Signal Processing, vol. 1, pp. 1–11 (1996)Google Scholar
  9. 9.
    Jetto, L., Orlando, G., Sanfilippo, A.: The edge point detection problem in image sequences: Definition and comparative evaluation of some 3D edge detecting schemes. In: Proc. of the 7th Mediterranean Conference on Control and Automation (MED 1999), pp. 2161–2171 (1999)Google Scholar
  10. 10.
    Mortensen, P.P., Bro, R.: Real time monitoring and chemical profiling of a cultivation process. Chem. and Intell. Lab. Syst. 84(1-2), 106–113 (2005)CrossRefGoogle Scholar
  11. 11.
    Busscher, N., Kahl, J., Andersen, J., Huber, M., Mergardt, G., Doesburg, P., Paulsen, M., Ploeger, A.: Standardization of the biocrystallization method for carrot samples. Biological Agriculture and Horticulture 27, 1–23 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Diana Porro-Muñoz
    • 1
    • 2
  • Robert P. W. Duin
    • 2
  • Mauricio Orozco-Alzate
    • 3
  • Isneri Talavera Bustamante
    • 1
  1. 1.Advanced Technologies Application Center (CENATAV)Cuba
  2. 2.Pattern Recognition LabTU DelftThe Netherlands
  3. 3.Universidad Nacional de ColombiaSede ManizalesColombia

Personalised recommendations