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Using Fisher Kernel on 2D-Shape Identification

  • Carlos M. Travieso
  • Juan C. Briceño
  • Miguel A. Ferrer
  • Jesús B. Alonso
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4739)

Abstract

This paper proposes to use the Fisher kernel for planar shape recognition. A synthetic experiment with artificial shapes has been built. The difference among shapes is the number of vertexes, links between vertexes, size and rotation. The 2D-shapes are parameterized with sweeping angles in order to obtain scale and rotation invariance. A Hidden Markov Model is used to obtain the Fisher score which feeds the Support Vector Machine based classifier. Noise has been added to the shapes in order to check the robustness of the system against noise. Hit ratio score over 99%, has been obtained, which shows the ability of the Fisher kernel tool for planar shape recognition.

Keywords

2D-shape recognition 2D-shape Hidden Markov Models (HMM) Support Vector Machines (SVM) Fisher Kernel 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Carlos M. Travieso
    • 1
  • Juan C. Briceño
    • 2
  • Miguel A. Ferrer
    • 1
  • Jesús B. Alonso
    • 1
  1. 1.CEntro Tecnológico para la Innovación en Comunicaciones (CETIC) Dpto. de Señales y Comunicaciones, Universidad de Las Palmas de Gran Canaria Campus de Tafira, E35017, Las Palmas de Gran CanariaSpain
  2. 2.Escuela de Ciencias de la Computación e Informática. Universidad de Costa Rica. Sede “Rodrigo Facio Brenes”, Montes de Oca, 2060, San José, Costa Rica 

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