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Dictionary Learning

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Book cover Sparse Representations and Compressive Sensing for Imaging and Vision

Part of the book series: SpringerBriefs in Electrical and Computer Engineering ((BRIEFSELECTRIC))

Abstract

Instead of using a pre-determined dictionary B, as in (5.1), one can directly learn it from the data [99]. Indeed, it has been observed that learning a dictionary directly from the training data rather than using a predetermined dictionary usually leads to better representation and hence can provide improved results in many practical image processing applications such as restoration and classification [121], [155], [100],,[40], [107], [131], [133]. In this section, we will highlight some of the methods for learning dictionaries and present their applications in object representation and classification.

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  1. Center for Automation Research, University of Maryland, A.V. Williams Building, College Park, MD, USA

    Vishal M. Patel

  2. Department of Electrical and Computer Engineering and Center for Automation Research, University of Maryland, A.V. Williams Building, College Park, MD, USA

    Rama Chellappa

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Patel, V.M., Chellappa, R. (2013). Dictionary Learning. In: Sparse Representations and Compressive Sensing for Imaging and Vision. SpringerBriefs in Electrical and Computer Engineering. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6381-8_6

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