Using Fisher Kernel on 2D-Shape Identification
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.
Keywords2D-shape recognition 2D-shape Hidden Markov Models (HMM) Support Vector Machines (SVM) Fisher Kernel
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- 8.Briceño, J.C., Travieso, C.M., Ferrer, M.A., Alonso, J.B., Briceño R.D.: A genus recognition system for the Costa Rica Lauraceae Family, using a Support Vector Machine. XXIII Conferencia Latinoamericana de Informática, p. 120 (abstract) (2006)Google Scholar
- 9.Rabinier, L., Juang, B.H.: Fundamentals of Speech Recognition. Prentice-Hall, Englewood Cliffs, New Jersey (1993)Google Scholar
- 10.David, S., Ferrer, M.A., Travieso, C.M., Alonso, J.B.: gpdsHMM: A Hidden Markov Model Toolbox in the Matlab Environment. CSIMTA, Complex Systems Intelligence and Modern Technological Applications, 476-479 (2004)Google Scholar
- 11.Joachims, T.: Making large-Scale SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning. MIT Press, Cambridge (1999)Google Scholar