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.
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Travieso, C.M., Briceño, J.C., Ferrer, M.A., Alonso, J.B. (2007). Using Fisher Kernel on 2D-Shape Identification. In: Moreno Díaz, R., Pichler, F., Quesada Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2007. EUROCAST 2007. Lecture Notes in Computer Science, vol 4739. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75867-9_93
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DOI: https://doi.org/10.1007/978-3-540-75867-9_93
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75866-2
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