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Dissimilarity-Based Classification of Multidimensional Signals by Conjoint Elastic Matching: Application to Phytoplanktonic Species Recognition

  • Émilie Caillault
  • Pierre-Alexandre Hébert
  • Guillaume Wacquet
Part of the Communications in Computer and Information Science book series (CCIS, volume 43)

Abstract

The paper describes a classification method of multidimensional signals, based upon a dissimilarity measure between signals. Each new signal is compared to some reference signals through a conjoint dynamic time warping algorithm of their time features series, of which proposed cost function gives out a normalized dissimilarity degree. The classification then consists in presenting these degrees to a classifier, like k-NN, MLP or SVM. This recognition scheme is applied to the automatic estimation of the Phytoplanktonic composition of a marine sample from cytometric curves. At present, biologists are used to a manual classification of signals, that consists in a visual comparison of Phytoplanktonic profiles. The proposed method consequently provides an automatic process, as well as a similar comparison of the signal shapes. We show the relevance of the proposed dissimilarity-based classifier in this environmental application, and compare it with classifiers based on the classical DTW cost-function and also with features-based classifiers.

Keywords

Dynamic Time Warping Dissimilarity Measure Paired Point Emiliania Huxleyi Cost Dist 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Émilie Caillault
    • 1
  • Pierre-Alexandre Hébert
    • 1
  • Guillaume Wacquet
    • 1
  1. 1.Laboratoire d’Analyse des Systèmes du Littoral, EA 2600, ULCO, Maison de la Recherche Blaise PascalUniversité Lille Nord de FranceCalais CedexFrance

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