Fundamentals of speech recognition
Fundamentals of speech recognition
Making large-scale support vector machine learning practical
Advances in kernel methods
Hidden Markov Models with Spectral Features for 2D Shape Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Investigating Hidden Markov Models' Capabilities in 2D Shape Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
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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.