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)


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.



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.


  1. 1.
    Albarrak, A., Coenen, F., Zheng, Y.: Classification of volumetric retinal images using overlapping decomposition and tree analysis. In: Proceedings of 26th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2013), pp. 11–16 (2013)Google Scholar
  2. 2.
    Albarrak, A., Coenen, F., Zheng, Y.: Volumetric image classification using homogeneous decomposition and dictionary learning: a study using retinal optical coherence tomography for detecting age-related macular degeneration. J. Comput. Med. Imaging Graph. 55, 113–123 (2016)CrossRefGoogle Scholar
  3. 3.
    Amaral, S., Monteiro, A.V.M., Câmara, G., Quintanilha, J.A.: DMSP/OLS night time light imagery for urban population estimates in the Brazilian Amazon. Int. J. Remote Sens. 27(5), 855–870 (2006)CrossRefGoogle Scholar
  4. 4.
    Al Salman, A.S., Ali, A.E.: Population estimation from high resolution satellite imagery: a case study from Khartoum. Emir. J. Eng. Res. 16(1), 63–69 (2011)Google Scholar
  5. 5.
    Berndt, D.j., Clifford, J.: Using dynamic time warping to find patterns in time series. In: Proceedings of AAAI Workshop on Knowledge Discovery in Databases, pp 229–248 (1994)Google Scholar
  6. 6.
    Cheng, L., Zhou, Y., Wang, L., Wang, S., Du, C.: An estimate of the city population in China using DMSP night-time satellite imagery. In: Proceedings of IEEE International Conference on Geoscience and Remote Sensing Symposium (IGARSS), pp 691–694 (2007)Google Scholar
  7. 7.
    Dittakan, K.: Population estimation mining from satellite imagery. Ph.D. thesis, University of Liverpool (2015)Google Scholar
  8. 8.
    Dittakan, K., Coenen, F.: Early Detection of Osteoarthritis Using Local Binary Patterns: A Study Directed at Human Joint Imagery. In: Booth, R., Zhang, M.-L. (eds.) PRICAI 2016. LNCS (LNAI), vol. 9810, pp. 93–105. Springer, Cham (2016). Scholar
  9. 9.
    Elsayed, A., Hijazi, M.H.A., Coenen, F., García-Fiñana, M., Sluming, V., Zheng, Y.: Classification of MRI brain scan data using shape criteria. Ann. Br. Mach. Vis. Assoc. (BMVA) 2011(6), 1–14 (2011)Google Scholar
  10. 10.
    Elsayed, A., Coenen, F., García-Fiñana, M., Sluming, V.: Region of interest based image classification: a study in MRI brain scan categorization. In: Karahoca, A. (ed.) Data Mining Applications in Engineering and Medicine, pp. 225–248. InTech - Open Science, Slavka Krautzeka (2012)Google Scholar
  11. 11.
    El Salhi, S., Coenen, F., Dixon, C., Khan, M.: Predicting springback using 3D surface representation techniques: a case study in sheet metal forming. J. Expert Syst. Appl. 42(1), 79–93 (2014)CrossRefGoogle Scholar
  12. 12.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann, Burlington (2011)zbMATHGoogle Scholar
  13. 13.
    Haverkamp, D.: Automatic building extraction from IKONOS imagery. In: Proceedings of Annual Conference of the American Society for Photogrammetry and Remote Sensing (2004)Google Scholar
  14. 14.
    Hijazi, M.H.A., Coenen, F., Zheng, Y.: Data mining techniques for the screening of age-related macular degeneration. J. Knowl. Based Syst. 29, 83–92 (2012)CrossRefGoogle Scholar
  15. 15.
    Hijazi, M.H.A., Coenen, F., Zheng, Y.: Data mining for AMD screening: a classification based approach. Int. J. Simul. Syst. Sci. Technol. (IJSSST) 15(2), 64–68 (2015)Google Scholar
  16. 16.
    Hamza, I.A., Iyela, A.: Land use pattern, climate change, and its implication for food security in Ethiopia: a review. Ethiop. J. Env. Stud. Manag. 5, 26–31 (2012)Google Scholar
  17. 17.
    Huan, J., Wang, W., Prins, J.: Efficient mining of frequent subgraph in the presence of isomorphism. In: Proceedings of the 2003 International Conference on Data Mining (ICDM 2003), pp. 549–561 (2003)Google Scholar
  18. 18.
    Javed, Y., Khan, M.M., Chanussot, J.: Population density estimation using textons. In: Proceedings of IEEE International Conference on Geoscience and Remote Sensing Symposium (IGARSS 2012), pp. 2206–2209 (2012)Google Scholar
  19. 19.
    Jiang, C., Coenen, F., Zito, M.: A survey of frequent subgraph mining algorithms. Knowl. Eng. Rev. 28(1), 75–105 (2013)CrossRefGoogle Scholar
  20. 20.
    Karter, J.: Time Series Analysis with MATLAB. CreateSpace Independent Publishing Platform (2016)Google Scholar
  21. 21.
    Khan, M., Coenen, F., Dixon, C., El Salhi, S., Penalva, M., Rivero, A.: An intelligent process model: predicting springback in single point incremental forming. Int. J. Adv. Manuf. Technol. 76, 2071–2082 (2015)CrossRefGoogle Scholar
  22. 22.
    Kraus, S.P., Senger, L.W., Ryerson, J.M.: Estimating population from photographically determined residential land use types. J. Remote Sens. Environ. 3(1), 35–42 (1974)CrossRefGoogle Scholar
  23. 23.
    Krizhevsky, A., Sutskever. I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: Proceedings of NIPS (2012)Google Scholar
  24. 24.
    Li, G., Wang, Q.: Using Landsat ETM+ imagery to measure population density in Indianapolis, Indiana, USA. J Photogramm. Eng. Remote Sens. 71(8), 63–69 (2005)Google Scholar
  25. 25.
    Liang, P., Li, S.F., Qin, J.W.: Multi-resolution local binary patterns for image classification. In: Proceedings of the Twentieth International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR), pp. 164–169 (2010)Google Scholar
  26. 26.
    Liu, X., Clarke, K.: Estimation of residential population using high resolution satellite imagery. In: Proceedings of Third International Symposium on Remote Sensing of Urban Area, pp. 153–160 (2002)Google Scholar
  27. 27.
    Lo, C.: Zone-based estimation of population and housing units from satellite-generated land use/land cover maps. In: Mesev, V. (ed.) Remotely Sensed Cities, pp. 157–180. Taylor and Francis, London and New York (2003)Google Scholar
  28. 28.
    Ma, T., Zhou, C., Pei, T., Haynie, S., Fan, J.: Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data: a comparative case study from China’s cities. J. Remote Sens. Environ. 124, 99–107 (2012)CrossRefGoogle Scholar
  29. 29.
    Madden, P., Goodman, J., Green, J., Jenkinson, C.: Growing pains: population and sustainability in the UK. Technical report, Forum for the Future (2010)Google Scholar
  30. 30.
    Mather, M., Pollard, K., Jacobsen, L.A.: Report on America: first results from the 2010 census. Technical report, Population Reference Bureau, Washington, DC, USA (2011)Google Scholar
  31. 31.
    Montanvert, A., Meer, P., Rosenfield, R.: Hierarchical image analysis using irregular tessellations. IEEE Trans. Pattern Anal. Mach. Intell. 13(4), 307–316 (1991)CrossRefGoogle Scholar
  32. 32.
    Myers, C.S., Rabiner, L.R.: A comparative study of several dynamic time-warping algorithms for connected word recognition. Bell Syst. Tech. J. 60(7), 1389–1409 (1981)CrossRefGoogle Scholar
  33. 33.
    Office for National Statistics: National population projections, 2010-based statistical bulletin. Technical report, Office for National Statistics (2011)Google Scholar
  34. 34.
    Pietikäinen, M.: Image analysis with local binary patterns. In: Kalviainen, H., Parkkinen, J., Kaarna, A. (eds.) SCIA 2005. LNCS, vol. 3540, pp. 115–118. Springer, Heidelberg (2005). Scholar
  35. 35.
    Pink, B.: Census of population and housing: nature and content Australia 2011. Technical report, Australian Bureau of Statistics (2008)Google Scholar
  36. 36.
    Pozzi, F., Small, C., Yetman, G.: Modeling the distribution of human population with night-time satellite imagery and gridded population of the world. In: Proceedings of Future Intelligent Earth Observing Satellites Conference (2002)Google Scholar
  37. 37.
    Pratt, H., Coenen, F., Broadbent, D.M., Harding, S.P., Zheng, Y.: Convolutional neural networks for diabetic retinopathy. Procedia Comput. Sci. 90, 200–205 (2016). (In: Proceedings of Medical Image Understanding and Analysis (MIUA 2016))CrossRefGoogle Scholar
  38. 38.
    Samet, H.: The quadtree and related hierarchical data structures. ACM Comput. Surv. 16(2), 187–260 (1984)MathSciNetCrossRefGoogle Scholar
  39. 39.
    Sutton, P.: Modeling population density with night-time satellite imagery and GIS. Comput. Environ. Urban Syst. 21, 227–244 (1997)CrossRefGoogle Scholar
  40. 40.
    Tadmor, E., Nezzar, S., Vese, L.: Multiscale hierarchical decomposition of images with applications to deblurring, denoising and segmentation. Commun. Math. Sci. 6(2), 281–307 (2008)MathSciNetCrossRefGoogle Scholar
  41. 41.
    Udomchaiporn, A., Coenen, F., García-Fiñana, M., Sluming, V.: 3-D volume of interest based image classification. In: Booth, R., Zhang, M.-L. (eds.) PRICAI 2016. LNCS (LNAI), vol. 9810, pp. 543–555. Springer, Cham (2016). Scholar
  42. 42.
    Wu, S.S., Qiu, X., Wang, L.: Population estimation methods in GIS and remote sensing: a review. J. GISci. Remote Sens. 42(1), 80–96 (2005)CrossRefGoogle Scholar
  43. 43.
    Zhang, Y., Zhang, B., Coenen, F., Lu, W.: Breast cancer diagnosis from biopsy images with highly reliable random subspace classifier ensembles. Mach. Vis. Appl. 24, 1405–1420 (2013)CrossRefGoogle Scholar
  44. 44.
    Zhang, Y., Zhang, B., Coenen, F., Xiao, J., Lu, W.: One-class kernel subspace ensemble for medical image classification. EURASIP J. Adv. Sig. Process. 17, 1–13 (2014)Google Scholar
  45. 45.
    Zheng, Y., Hijazi, M.H.A., Coenen, F.: Automated “Disease/No Disease” grading of age-related macular degeneration by an image mining approach. Investig. Ophthalmol. Vis. Sci. 53(13), 8310–8318 (2012)CrossRefGoogle Scholar

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© 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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