1887
Volume 34, Issue 3
  • ISSN: 0812-3985
  • E-ISSN: 1834-7533

Abstract

Image retrieval systems provide an effective tool for signature mapping and retrieval that can be applied to magnetic images to assist with preliminary interpretation of large datasets. Image retrieval is currently a very active field of research, motivated by the significant increase in the size of digital image databases in a wide range of image-based fields. It has emerged as a powerful tool for searching and locating a desired image, or part-image, from a large image database.

Most existing image retrieval systems characterise the content of an image using low-level visual features such as colour, shape, texture, and spatial relationships between objects in the image. This approach is known as Content-Based Image Retrieval (CBIR). The objective of CBIR is to efficiently find and retrieve those images from a database that are most similar to the user’s query image. The challenge when applying CBIR to new types of images is deciding how best to characterise the content of an image so that the results are useful and meaningful.

We have developed a model for content-based magnetic image retrieval (CBMIR) using intensity, texture, and shape descriptors. Region and boundary-based shape information is extracted using various edge detection techniques, and texture content is derived using statistical and wavelet transform-based methods. The model has been incorporated into a MATLAB-based system for image retrieval, and results using an experimental magnetic database are presented. The system is interactive, allowing the user’s intentions to be incorporated into the retrieval results.

We introduce image retrieval as an analysis tool that has been widely adopted in other image-based fields and that has much scope to be further developed and refined for geophysical applications.

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/content/journals/10.1071/EG03195
2003-06-01
2026-01-18
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References

  1. Aksoy, S. and Haralick, R.M., 2000, Using Texture in Image Similarity and Retrieval: in Pietikainen, M. (ed.), Texture Analysis in Machine Vision, Series in Machine Perception and Artificial Intelligence, 20, 129–149.
  2. Alber, I.E., Xiong, Z., Yeager, N., Farber, M., and Pottenger, W.M., 2001, Fast Retrieval of Multi- and Hyperspectral Images Using Relevance Feedback: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 3, 1149–1151.
  3. Allen, J.F., 1983, Maintaining knowledge about temporal intervals: Communications of the ACM, 26, 832–843.
  4. Behr, T. and Schneider, M., 2001, Topological Relationships of Complex Points and Complex Regions: 20th International Conference on Conceptual Modeling (ER), 56–59.
  5. Buckingham, A., 2003, Edge Detection & Image Retrieval Techniques in the Interpretation of Magnetic Images For Mineral Exploration: PhD Thesis, The University of Western Australia.
  6. Burl, M., Fayyad, U., Perona, P., and Smyth, P., 1996, Trainable cataloging for digital image libraries with applications to volcano detection: California Institute of Technology Technical Report CNS-TR-96-01.
  7. Chang, S.K., Shi, Q.Y., and Yan, C.Y., 1987, Iconic Indexing by 2D strings: IEEE Transactions on Pattern Analysis and Machine Intelligence, 9, 413–428.
  8. Chang, T. and Kuo, C.-C.J., 1993, Texture analysis and classification with tree- structured wavelet transform: IEEE Transactions on Image Processing, 2, 429–441.
  9. Daubechies, I. and Sweldens, W., 1998, Factoring wavelet transforms into lifting steps: J. Fourier Anal. Appl., 4, 247–269.
  10. Del Bimbo, A., 1999, Visual Information Retrieval: Morgan Kaufmann Publishers. Dentith, M.C., 1995, Textural filtering of aeromagnetic data: Exploration Geophysics, 26, 209–214.
  11. Do, M., Ayer, S. and Vetterli, M., 1999, Invariant Image Retrieval using Wavelet Maxima Moment: Proc. of 3rd International Conference on Visual Information and Information Systems, 451–458.
  12. Eakins, J.P. and Graham, M.E., 1999, Content-based Image Retrieval: A Report to the JISC Technology Applications Programme: Inst. for Image Data Research, Univ. of Northumbria at Newcastle.
  13. Gettings, M.E., 1999, Using textural measures of aeromagnetic data to infer lithology: International Union of Geodesy and Geophysics XXII General Assembly Abstracts, A.390.
  14. Haralick, R.M., Shanmugan, K., and Dinstein, I., 1973, Texture features for image classification: IEEE Transactions Systems, Man and Cybernetics, 3, 610–621.
  15. Livens, S., Scheunders, P., Van de Wouwer, G. and Van Dyck, D., 1997, Wavelets for Texture Analysis: Proceedings of the 6th IEEE International Conference, 814–816.
  16. Loncaric, S., 1998, A survey of shape analysis techniques: Pattern Recognition, 31, 983–1001.
  17. Manjunath, B. and Ma, W.Y., 1996, Texture features for browsing and retrieval of image data: IEEE Transactions on Pattern Analysis and Machine Intelligence, 18, 837–842.
  18. Mehtre, B.M., Kankanhalli, M.S., and Lee, W.F., 1997, Shape measures for content based image retrieval: a comparison: Information processing & management, 33, 319–337.
  19. Miller, H.G. and Singh, V., 1994, Potential field tilt—a new concept for location of potential field sources: Journal of Applied Geophysics, 32, 213–217.
  20. Nabil, M., Ngu, A.H.H., and Shepherd, J., 1996, Picture Similarity Retrieval Using the 2D Projection Interval Representation: IEEE Transactions on Knowledge and Data Engineering, 8, 533–539.
  21. Petrakis, E., 1992, Image Representation, Indexing and Retrieval based on Spatial Relationships and Properties of Objects. Ph.D. Thesis, University of Crete at Heraklion.
  22. Roest, W.R., Pilkington, M., 1993, Identifying remanent magnetization effects in magnetic data: Geophysics, 58, 653–659.
  23. Rosati, I. and Cardarelli, E., 1997, Statistical pattern recognition techniques to enhance anomalies in magnetic surveys: Journal of Applied Geophysics, 37, 55–66.
  24. Rui, Y., Huang, T.S. and Mehrotra, S., 1998, Relevance Feedback Techniques in Interactive Content-Based Image Retrieval: in Sethi, I.K and Jain, R. (eds.), Storage and Retrieval for Image and Video Databases, 25–36.
  25. Smeulders, A., Worring, M., Santini, S., Gupta, A. and Jain, R., 2000, Content-Based Image Retrieval at the End of the Early Years: IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 1349–1380.
  26. Smith, J.R., 1997, Integrated Spatial and Feature Image Systems: Retrieval, Analysis and Compression. Ph.D. Thesis, Columbia University.
  27. Smith, R.S., Thurston, J.B., Dai, T-F., and Macleod, I., 1998, iSPI - the improved source parameter imaging method: Geophysical Prospecting, 46, 141–151.
  28. Tuceryan, M. and Jain, A.K., 1998, Texture analysis: in Chen, C.H., Pau, L.F., and Wang, P.S.P. (eds.), Handbook of Pattern Recognition and Computer Vision (Second Edition), World Scientific Publishing Co, 235–276.
  29. Van de Wouwer, G., Scheunders, P., and Van Dyck, D., 1999, Statistical texture characterization from discrete wavelet representations: IEEE Transactions on Image Processing, 8, 592–598.
  30. Yunsheng, S., Strangway, D.W., and Urquhart, W.E.S., 1985, Geological interpretation of a high-resolution aeromagnetic survey in the Amos-Barraute area of Quebec: in Hinze, W.J. (ed.), The Utility of Regional Gravity and Magnetic Anomaly Maps, Society of Exploration Geophysicists.
  31. Zhang, Q-L., and Chang, S-K., and Yau, S.S-T., 1996, A unified approach to Iconic Indexing, Retrieval, and Maintenance of Spatial Relationships in Image Databases: Journal of Visual Communication and Image Representation, 7, 307–324.
  32. Zhou, S., Venkatesh, Y.V., and Ko C.C., 2000, Texture retrieval using tree-structured wavelet transform: in Proceedings of International Conference on Computer Vision, Pattern Recognition, Image Processing (CVPRIP’2000).
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