A new method for supervised texture classification, denoted by frame texture classification method (FTCM), is proposed. The method is based on a deterministic texture model in which a small image block, taken from a texture region, is modeled as a sparse linear combination of frame elements. FTCM has two phases. In the design phase a frame is trained for each texture class based on given texture example images. The design method is an iterative procedure in which the representation error, given a sparseness constraint, is minimized. In the classification phase each pixel in a test image is labeled by analyzing its spatial neighborhood. This block is represented by each of the frames designed for the texture classes under consideration, and the frame giving the best representation gives the class. The FTCM is applied to nine test images of natural textures commonly used in other texture classification work, yielding excellent overall performance.
The recently presented recursive least squares dictionary learning algorithm (RLS-DLA) is tested in a general image compression application. Dictionaries are learned in the pixel domain and in the 9/7 wavelet domain, and then tested in a straightforward compression scheme. Results are compared with state-of-the-art compression methods. The proposed compression scheme using RLS-DLA learned dictionaries in the 9/7 wavelet domain performs better than using dictionaries learned by other methods. The compression rate is just below the JPEG-2000 rate which is promising considering the simple entropy coding used.Index Terms-dictionary learning, RLS-DLA, sparse approximation, overcomplete dictionary, image compression
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