Double-Density Discrete Wavelet Transform Based Texture Classification

Texture classification plays an important role in image analysis. The wavelet is a very efficient multiscale analysis method that has been successfully applied to describe the texture. However, it is translation-invariant. The recent double-density discrete wavelet transform have two interesting property, low computational complexity and nearly shift invariant. In this paper, the texture feature based on the double-density discrete wavelet transform are derived from the subbands and tested in two texture databases. The experimental results suggest the potential capacity of the double-density discrete wavelet transform in texture analysis.