A Co-Training Based Semi-Supervised Human Action Recognition Algorithm

Abstract

A novel semi-supervised algorithm based on co-training is proposed in this paper. In the method, the motion energy history image are used as the different feature representation of human action; then the co-training based semi-supervised learning algorithm is utilized to predict the category of unlabeled training examples. And the average motion energy and history images are calculated as the recognition model for each category action. When recognition, the observed action is firstly classified through its correlation coefficients to the prior established templates respectively; then its final category is determined according to the consistency between the classification results of motion energy and motion history images. The experiments on Weizmann dataset demonstrate that our method is effective for human action recognition.

Semi-supervised LIBS quantitative analysis method based on co-training regression model with selection of effective unlabeled samples

“…The main objective of semi-supervised learning is to obtain a good performance and generalization ability under the condition that the reference-valued information is missing for unlabeled training samples. The achievement of semisupervised learning has been widely used in various fields of digital image processing [13], human identity recognition [14], statistical question classification [15] and human action recognition [16]. The co-training algorithm is a powerful semi-supervised learning method [17] in which two k-nearest neighbor (KNN) regressors with different distance metrics are designed as basic learners and each regressor labels the unlabeled examples for the other regressor iteratively.…”