Texture Analysis and Classification Using Bottom-Up Tree-Structured Wavelet Transform

Abstract

One difficulty in texture analysis was the lack of adequate tools for characterizing textures. Recent developments in multiresolution analysis, such as the wavelet transform, attempt to overcome this difficulty. The tree-structured wavelet transform (TSWT), which affords for an analysis in terms of the texture’s decomposition structure and the corresponding spectrum in the space-frequency domain, has received a lot of attention. Recently, Chang [1] proposed a texture classification algorithm based on a top-down TSWT (TSWTTD). Although high classification rates were reported, its performance is highly dependent on a set of parameters which have to be determined through elaborate tuning. We propose a texture classification algorithm based on a bottom-up TSWT (TSWTBU), which is not dependent on ad-hoc parameters and shows also superior performance. We also provide an analysis of the structural stability of TSWT.KeywordsStructural StabilitySuperior PerformanceTexture AnalysisParent NodeMultiresolution AnalysisThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.