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The study of sparse representation of signals and images has attracted tremendous interest over the last decade. This is partly due to the fact that signals or images of interest, though high dimensional, can often be coded using few representative atoms in some dictionary. Olshausen and Field in their seminal work [3] introduced the idea of learning dictionary from data instead of using off-the-shelf bases. Since then, data-driven dictionaries have been shown to work well for both image restoration and classification tasks [4].
Given a set of examples Y = [y1, ⋯ , yn], the goal of dictionary learning algorithms such as KSVD...
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References
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Patel, V.M., Nguyen, H.V. (2020). Domain Adaptation Using Dictionaries. In: Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-030-03243-2_819-1
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DOI: https://doi.org/10.1007/978-3-030-03243-2_819-1
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