Texture Classification Using Wavelet Frame Representation Based Feature

Texture classification is an important component in image analysis and understanding. The wavelet, a multiresolution signal analysis technique, has been successfully applied to describe the texture. It is noticed that the wavelet transform modulus maximum and minimum can effectively characterize a signal. This paper employs the density of modulus maxima and the density of modulus minima of the wavelet frame representation as features for texture classification. In order to avoid the problem of curse of dimensionality, the feature selection algorithm, sequential forward floating selection (SFFS), is used to select feature subset. The experimental results on two benchmark databases indicate that the new feature is better than existing features based on modulus extrema, zero-crossings and local extrema.