Bark Classification Using RBPNN Based on Gabor Filter in Different Color Space
This paper proposed a new method of extracting texture features based on Gabor wavelet in different color space. In addition, the application of these features for bark classification applying radial basis probabilistic network (RBPNN) and SVM(support vector machine) has been used. To extract the bark texture features, Gabor filter the image has been filtered with four orientations and six scales filters, and then the mean and standard deviation of the image output are computed. In addition, apart from these features of parameter of Gabor filter features, other features such as color distribution angles were also extracted. Finally, the combined Gabor feature vectors and color distribution angles are fed up into RBPNN and SVM for classification. The performance of colour space features is found to be better than that of the features which just extracted from grey image. Experimental results show that features extracted using the proposed approach can be used for bark texture classification.
Gabor wavelet Radial basis probabilistic network Bark image Image recognition
Zhi-Kai Huang De-Shuang Huang Zhong-Hua Quan
Intelligent Computing Lab, Hefei Institute of Intelligent Machines Chinese Academy of Sciences, P.O.Box 1130 Hefei, Anhui 230031, China
国际会议
2006 IEEE International Conference on Information Acquisition
山东威海
英文
946-950
2006-08-20（万方平台首次上网日期，不代表论文的发表时间）