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In this paper we propose a framework for texture classification through filtering. Given a set of textures, the filters are derived as the independent components of the input images and each texture is then characterized by the marginal distributions of its filter responses. The marginal distributions provide a low-dimensional representation of images and result in a significant dimension reduction compared to the full joint distribution. When the components are independent, the dimension reduction does not incur any information loss for classification. The texture classification problem is posed as classifying the textures based on their marginal distributions. Preliminary results demonstrate significant improvement in texture classification performance.