Abstract
This paper presents a multi-layer dictionary learning method for classification tasks. The goal of the proposed multi-layer framework is to use the supervised dictionary learning approach locally on raw images in order to learn local features. This method starts by building a sparse representation at the patch-level and relies on a hierarchy of learned dictionaries to output a global sparse representation for the whole image. It relies on a succession of sparse coding and pooling steps in order to find an efficient representation of the data for classification. This method has been tested on a classification task with good results.
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Acknowledgement
This work has been partially supported by the LabEx PERSYVAL-Lab (ANR-11-LABX-0025-01).
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Chan Wai Tim, S., Rombaut, M., Pellerin, D. (2016). Multi-layer Dictionary Learning for Image Classification. In: Blanc-Talon, J., Distante, C., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2016. Lecture Notes in Computer Science(), vol 10016. Springer, Cham. https://doi.org/10.1007/978-3-319-48680-2_46
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DOI: https://doi.org/10.1007/978-3-319-48680-2_46
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