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

  • Frans CoenenEmail author
  • Kwankamon Dittakan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 914)

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.

Notes

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.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceThe University of LiverpoolLiverpoolUK
  2. 2.Faculty of Technology and EnvironmentPrince of Songkla University (PSU)PhuketThailand

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