IEEE Transactions on Multimedia
Design-based texture feature fusion using Gabor filters and co-occurrence probabilities
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
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.