Title :
SVR-parameters selection for image watermarking
Author :
Li, Chun-hua ; Lu, Zheng-Ding ; Zhou, Ke
Author_Institution :
Coll. of Comput. Sci. & Technol., Huazhong Univ. of Sci. & Technol.
Abstract :
An image digital watermarking technique using support vector regression (SVR) is proposed and researched in this paper. Firstly, the method of embedding and extracting watermarking from digital image is given. Then, the influence of SVR-learning parameters on the watermarking performance is analyzed, and the ideal value range of SVR-learning parameters for different images is given respectively. Finally, the results are validated with other images. Experimental results show that this technique can obtain good watermarking performance as well as good learning performance when RBF kernel is adopted with its width a from 8 to 10, balanceable parameter C from 0.8 to 1, insensitive parameter s from 0.008 to 0.01 respectively
Keywords :
learning (artificial intelligence); radial basis function networks; support vector machines; watermarking; RBF kernel; SVR-learning parameter; image digital watermarking technique; support vector regression; Computer science; Educational institutions; Image analysis; Kernel; Learning systems; Machine learning; Neural networks; Risk management; Support vector machines; Watermarking;
Conference_Titel :
Tools with Artificial Intelligence, 2005. ICTAI 05. 17th IEEE International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
0-7695-2488-5
DOI :
10.1109/ICTAI.2005.119
