Gabor小波变换代码用于局部特征提取使用，又相当好的效果-Gabor texture descr iptor have gained much attention for

different aspects of computer vision and pattern recognition.

Recently, on the rayleigh nature of Gabor filter outputs

Rayleigh model Gabor texture descr iptor is proposed.

In this paper, we investigate the performance of these two

Gabor texture descr iptor in texture classification. We built

a texture classification system based on BPNN, and use the

corresponding feature vector from traditional Gabor texture

descr iptor or Rayleigh model one as input of BPNN. We use

three datasets from the Brodatz album database. For all

the three datasets, the original texture images are subdivided

into non-overlapping samples of size 32 × 32. 50

of the total samples are used for training and the rest are

used for testing. We compare the system training time and

recognition accuracy between two Gabor texture descr iptor.

The experimental results show that, it takes more time when

using Rayleigh model Gabor texture descr iptor than tr