Bark Classification Based on Contourlet Filter Features Using RBPNN
This paper proposed a new method of extracting texture features based on contourlet domain in RGB color space. In addition, the application of these features for bark classification applying radial basis probabilistic network (RBPNN) has been introduced. In this method, the bark texture feature is firstly extracted by decomposing an image into 6 subbands using the 7-9 biorthogonal Debauches wavelet transform, where each subband is fed to the directional filter banks stage with 32 directions at the finest level, then the mean and standard deviation of the image output are computed. The obtained feature vectors are fed up into RBPNN for classification. Experimental results show that, features extracted using the proposed approach can be more efficient for bark texture classification than gray bark image.
KeywordsHide Layer Radial Basis Function Neural Network Average Recognition Rate Extract Texture Feature Directional Filter Bank
Unable to display preview. Download preview PDF.
- 2.Chi, Z., Houqiang, L., Chao, W.: Plant Species Recognition Based on Bark Patterns Using Novel Gabor Filter Banks. IEEE Int. Conf. Neural Networks & Signal Processing Nanjing, 1035–1038 (2003)Google Scholar
- 3.Cunha, J.B.: Application of Image Processing Techniques in the Characterization of Plant Leafs. International Symposium on Industrial Electronics 1, 612–616 (2003)Google Scholar
- 5.Po, D.D.-Y.: Image Modeling in Contourlet Domain, Master’s thesis, University of Illinois at Urbana-Champaign (2003)Google Scholar
- 7.Huang, D.S.: Systematic Theory of Neural Networks for Pattern Recognition. Publishing House of Electronic Industry of China, Beijing (1996)Google Scholar
- 8.Do, M.N.: Contourlet Toolbox, http://www.ifp.uiuc.edu/minhdo/software/