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Image Representation for Image Mining: A Study Focusing on Mining Satellite Images for Census Data Collection

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Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 914))

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Abstract

This paper firstly presents a taxonomy for mage representation in the context of image mining. The main premise being that the actual mining algorithms that may be used are well understood, it is the preprocessing of the image data that remains a challenge. The requirement for the output from this preprocessing is some image representation that us both sufficiently expressive while at the same time being compatible with the mining process to be applied. Three categories of representation are considered: (i) statistics-based, (ii) tree-based and (iii) point series based. The second contribution of this paper is an analysis of the proposed representations categories with respect to a novel image mining application, the collection of individual household census data from satellite imagery, more specifically Google earth satellite imagery. The representations are considered both in terms of generating census prediction models and in terms of applying such models for larger scale census prediction.

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Acknowledgements

The authors wold like to thank the following whose ideas helped formulate the contents of this paper: (i) Abdulrahman Albarrak from the Department of Computer Science at The University of Liverpool, (ii) Ashraf Elsayed from the Department of Computer Science at the University of Alexandria, (iii) Marta García-Fiñana from the Department of Biostatistics at the University of Liverpool, (iv) Hanafi Hijazi from the School of Engineering and Information Technology at the University of Malaysia Sabah, (v) Vanessa Sluming from the School of Health Science at the University of Liverpool, (vi) Akadej Udomchaiporn from King Mongkut’s Institute of Technology Ladkrabang and (vii) Yalin Zheng from the department of Eye and Vision Science at the Royal Liverpool University Hospital.

Author information

Authors and Affiliations

  1. Department of Computer Science, The University of Liverpool, Liverpool, L69 3BX, UK

    Frans Coenen

  2. Faculty of Technology and Environment, Prince of Songkla University (PSU), Phuket, Thailand

    Kwankamon Dittakan

Authors
  1. Frans Coenen
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  2. Kwankamon Dittakan
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Correspondence to Frans Coenen .

Editor information

Editors and Affiliations

  1. Instituto de Telecomunicações, Lisbon, Portugal

    Ana Fred

  2. Department of Software Technology, Delft University of Technology, Voorburg, Zuid-Holland, The Netherlands

    Jan Dietz

  3. Faculty of Exact Sciences and Engineering, University of Madeira, Funchal, Portugal

    David Aveiro

  4. Henley Business School, University of Reading, Reading, UK

    Kecheng Liu

  5. University of Coimbra, Coimbra, Portugal

    Jorge Bernardino

  6. Instituto Politecnico de Setúbal (IPS), Setúbal, Portugal

    Joaquim Filipe

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Coenen, F., Dittakan, K. (2019). Image Representation for Image Mining: A Study Focusing on Mining Satellite Images for Census Data Collection. In: Fred, A., Dietz, J., Aveiro, D., Liu, K., Bernardino, J., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2016. Communications in Computer and Information Science, vol 914. Springer, Cham. https://doi.org/10.1007/978-3-319-99701-8_1

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  • DOI: https://doi.org/10.1007/978-3-319-99701-8_1

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