Incremental clustering of gesture patterns based on a self organizing incremental neural network

Authors:
Shogo Okada;Toyoaki Nishida
Affiliations:
Dept. of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University;Dept. of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

This paper describes an incremental unsupervised clustering mechanism for sequence patterns arising from human gestures. Although self-organizing incremental neural network (SOINN) is known as a powerful tool for incremental unsupervised clustering, it is only applicable to static and fixed-length patterns. In this paper, we propose an extension to SOINN to handle dynamic sequence patterns of variable length. We use a Hidden Markov Model (HMM), as a pre-processor for SOINN, to map the variable-length patterns into fixed-length patterns. HMM contributes to robust feature extraction from sequence patterns, enabling similar statistical features to be extracted from sequence patterns of the same category. As a result of experiments with incremental clustering gesture data, we have found that HMM based SOINN (HB-SOINN) outperforms other methods.