A Transfer Knowledge Framework for Object Recognition of Infrared Image
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 expodments 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.
Object recognition Image processing Machine learning
Zhiping Dan Nong Sang Jing Hu Shuifa Sun
Institute for Pattern Recognition & Artificial Intelligence, HUST,Huazhong University of Science and Institute for Pattern Recognition & Artificial Intelligence, HUST,Huazhong University of Science and Institute of Intelligent Vision and Image Information,China Three Gorges University, Yichang 443002,
国际会议
第八届图像图形技术与应用学术会议(8th Conference on Image and Graphics Technologies and Applications)(IGTA2013)
北京
英文
209-214
2013-04-02（万方平台首次上网日期，不代表论文的发表时间）