Titlebook: Image Understanding using Sparse Representations; Jayaraman J. Tiagarajan,Karthikeyan Natesan Ramamu Book 2014 Springer Nature Switzerland

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Image Understanding using Sparse Representations978-3-031-02250-0Series ISSN 1559-8136 Series E-ISSN 1559-8144

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Book 2014ponent in image understanding, since they emulate the activity of neural receptors in the primary visual cortex of the human brain. Sparse methods have been utilized in several learning problems because of their ability to provide parsimonious, interpretable, and efficient models. Exploiting the spa

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Sparse Models in Recognition,nce, adapting this representative model to perform discriminative tasks requires the incorporation of supervisory information into the sparse coding and dictionary learning problems. By introducing the prior knowledge on the sparsity of signals into the traditional machine learning algorithms, novel discriminative frameworks can be developed.

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Dictionary Learning: Theory and Algorithms,are learned directly from the data result in an improved performance compared to both pre-defined as well as tuned dictionaries. This chapter will focus exclusively on learned dictionaries and their applications in various image processing tasks.

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1559-8136 ortant component in image understanding, since they emulate the activity of neural receptors in the primary visual cortex of the human brain. Sparse methods have been utilized in several learning problems because of their ability to provide parsimonious, interpretable, and efficient models. Exploiti

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