Texture is a key feature frequently used for image analysis and recognition,and wavelet transforms are common tools for image texture analysis and classifcation.However,wavelet-based texture classifcation methods usually neglect the information in the low-pass subband,and cannot capture piece-wise singularities contained in image texture.In this paper,we propose local energy histograms(LEHs) for modeling wavelet subband coefcients,Poisson mixture models(PMMs) for modeling contourlet subband features,and clustering for extracting contourlet subband features.Then,these modeling methods are utilized to texture classifcation.The LEH-based texture classifcation method alleviates the difculty of modeling wavelet subband coefcients,the PMM-based texture classifcation method is the frst to model contourlet subband features using Poisson mixture models,and the texture classifcation method based on clustering in contourelet subands is a fast classifcation approach.Experimental results reveal that our proposed methods outperform some current state-of-the-art texture classifcation methods.