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International Conference on Formal Concept Analysis

ICFCA 2014: Formal Concept Analysis pp 251–267Cite as

Multilayered, Blocked Formal Concept Analyses for Adaptive Image Compression

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8478))

Abstract

Formal Concept Analysis (FCA) decomposes a matrix into a set of sparse matrices capturing its underlying structure. A similar task for real-valued data, transform coding, arises in image compression. Existing cosine transform coding for JPEG image compression uses a fixed, decorrelating transform; however, compression is limited as images rarely consist of pure cosines. The question remains whether an FCA adaptive transform can be applied to image compression. We propose a multi-layer FCA (MFCA) adaptive ordered transform and Sequentially Sifted Linear Programming (SSLP) encoding pair for adaptive image compression. Our hypothesis is that MFCA’s sparse linear codes (closures) for natural scenes, are a complete family of ordered, localized, oriented, bandpass receptive fields, predicted by models of the primary visual cortex. Results on real data demonstrate that adaptive compression is feasible. These initial results may play a role in improving compression rates and extending the applicability of FCA to real-valued data.

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Author information

Authors and Affiliations

  1. Telecommunications Software & Systems Group, Waterford Institute of Technology, Ireland

    Ruairí de Fréin

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  1. Ruairí de Fréin
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Editor information

Editors and Affiliations

  1. Technische Universität Dresden, 01062, Dresden, Germany

    Cynthia Vera Glodeanu

  2. INSA-Lyon, CNRS, LIRIS UMR 5205, 9621, 69621, Lyon, France

    Mehdi Kaytoue

  3. Babes-Bolyai University, 400084, Cluj, Romania

    Christian Sacarea

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de Fréin, R. (2014). Multilayered, Blocked Formal Concept Analyses for Adaptive Image Compression. In: Glodeanu, C.V., Kaytoue, M., Sacarea, C. (eds) Formal Concept Analysis. ICFCA 2014. Lecture Notes in Computer Science(), vol 8478. Springer, Cham. https://doi.org/10.1007/978-3-319-07248-7_18

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  • DOI: https://doi.org/10.1007/978-3-319-07248-7_18

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07247-0

  • Online ISBN: 978-3-319-07248-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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