
TY  - JOUR
A1  - Buckingham, A. J. 
A1  - Dentith, M. C. 
A1  - List, R. D. 
T1  - Semi-Automated Magnetic Image Retrieval
JF  - ASEG Extended Abstracts
JO  - ASEG Extended Abstracts
Y1  - 2003
VL  - 2003
IS  - 2
SP  - 1
EP  - 5
KW  - 
UR  - https://doi.org/10.1071/ASEG2003AB019 
N2  - 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. Locating discrete circular anomalies sought after when exploring for kimberlites is an example of a potential geophysical application. A model for content-based magnetic image retrieval (CBMIR) using texture and shape descriptors has been developed. 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 users intentions to be incorporated into the retrieval results. Tests an the experimental magnetic database, demonstrate that CBIR has the potential to be a powerful tool in magnetic image interpretation, as it has been in other image-based fields. 
ER  -

