Learning-Based Hand Sign Recognition Using SHOSLIF-M
نویسندگان
چکیده
In this paper, we present a self-organizing framework called the SHOSLIF-M for learning and recognizing spa-tiotemporal events (or patterns) from intensity image sequences. The proposed framework consists of a mul-ticlass, multivariate discriminant analysis to automatically select the most discriminating features (MDF), a space partition tree to achieve a logarithmic retrieval time complexity for a database of n items, and a general interpolation scheme to do view inference and generalization in the MDF space based on a small number of training samples. The system is tested to recognize 28 diierent hand signs. The experimental results show that the learned system can achieve a 96% recognition rate for test sequences that have not been used in the training phase.
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