In this paper, a human action recognition method using a hybrid neu-
ral network is presented. The method consists of three stages: preprocessing,
feature extraction, and pattern classification. For feature extraction, we propose
a modified convolutional neural network (CNN) which has a three-dimensional
receptive field. The CNN generates a set of feature maps from the action de-
scriptors which are derived from a spatiotemporal volume. A weighted fuzzy
min-max (WFMM) neural network is used for the pattern classification stage.
We introduce a feature selection technique using the WFMM model to reduce
the dimensionality of the feature space. Two kinds of relevance factors between
features and pattern classes are defined to analyze the salient features.