Abstract
Speed Up Robust Feature (SURF) and Local Contour Sequence(LCS) are methods used for feature extraction techniques for dynamic gesture recognition. A problem presented by these techniques is the large amount of data in the output vector which difficult the classification task. This paper presents a novel method for dimensionality reduction of the features extracted by SURF and LCS, called Convexity Approach. The proposed method is evaluated in a gesture recognition task and improves the recognition rate of LCS while SURF while decreases the amount of data in the output vector.
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Barros, P.V.A., Júnior, N.T.M., Bisneto, J.M.M., Fernandes, B.J.T., Bezerra, B.L.D., Fernandes, S.M.M. (2013). An Effective Dynamic Gesture Recognition System Based on the Feature Vector Reduction for SURF and LCS. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds) Artificial Neural Networks and Machine Learning – ICANN 2013. ICANN 2013. Lecture Notes in Computer Science, vol 8131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40728-4_52
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DOI: https://doi.org/10.1007/978-3-642-40728-4_52
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