Comparison of Two Methods for Texture Image Classification
Yang Zhang, Rongyi He, Muwei Jian · 2009
As the development of computer vision, texture becomes a key component for human visual perception and plays an important role in image-related applications. This paper compares two methods for texture image classification. The first scheme has an advantage that all the texture features are derived from wavelet transform, and it can reduce the time complexity of texture features extraction. The other method combines perceptual texture features and Gabor wavelet features for texture image classification. We test our proposed method using the Brodatz texture database, and the experimental results are compared.