Learning-based hand sign recognition using SHOSLIF-M

Authors:
Yuntao Cui;D. L. Swets;J. J. Weng
Affiliations:
-;-;-
Venue:
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Year:
1995

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Abstract

We present a self-organizing framework called the SHOSLIF-M for learning and recognizing spatiotemporal events (or patterns) from intensity image sequences. The proposed framework consists of a multiclass, 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 different 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.