Incremental clustering of gesture patterns based on a self organizing incremental neural network
Incremental clustering of gesture patterns based on a self organizing incremental neural network
复制标题
基于自组织增量神经网络的手势模式增量聚类
DOI：
10.1109/ijcnn.2009.5178845
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
T. Nishida
中科院分区：
文献类型：
--
作者：
S. Okada;T. Nishida
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.
