A transfer knowledge framework for object recognition of infrared image

Abstract

In the object recognition process of infrared image, as the amount of training data is very small, traditional learning does not construct a high-quality classifier for the recognition object. Aimed at the problem, a transfer knowledge framework for object recognition of infrared image is proposed in this paper. Hu moments is firstly extracts as feature vectors of object data, and then a large amount of exist object data with different distributions to the recognition object data is seen as the auxiliary training data in the feature spaces. Our transfer knowledge approach can transfer knowledge from the auxiliary data to help the tiny amount of training data to train a better classifier, which improve the performance of object recognition. According to the experiments in infrared images, it shows that the accuracy of object recognition has been greatly improved by our proposed approach compared with the other classical methods. © Springer-Verlag Berlin Heidelberg 2013.