Locally orderless images are families of three intertwined scale spaces that describe local histograms. We generalize locally orderless images by considering local histograms of a collection of filtered versions of the image, and by extending them to joint probability distributions. These constructions can be used to derive texture features and are shown to be a more general description of two established texture classification methods, viz., filter bank methods and cooccurrence matrices. Because all scale parameters are stated explicitly in this formulation, multi-resolution feature sets can be extracted in a systematic way. This includes new types of multi-resolution analysis, not only based on the spatial scale, but on the window size and intensity scale as well. Each multi-resolution approach improves texture classification performance, the best result being obtained if a multi-resolution approach for all scale parameters is used. This is demonstrated in experiments on a large data set of 1152 images for 72 texture classes.