Abstract
The measuring of interest and relevance have always been some of the main concerns when analyzing the results of a Content-Based Image-Retrieval (CBIR) system. In this work, we present a unique problem that the Solar Dynamics Observatory (SDO) CBIR system encounters: too many highly similar images. Producing over 70,000 images of the Sun per day, the problem of finding similar images is transformed into the problem of finding similar solar events based on image similarity. However, the most similar images of our dataset are temporal neighbors capturing the same event instance. Therefore a traditional CBIR system will return highly repetitive images rather than similar but distinct events. In this work we outline the problem in detail, present several approaches tested in order to solve this important image data mining and information retrieval issue.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Adams, R., Bischof, L.: Seeded region growing. IEEE Trans. Pattern Anal. Mach. Intell. 16(6), 641–647 (1994)
ADBIS. Adbis 2013 website (2013), http://www.jmbanda.com/ADBIS2013
Banda, J.M., Angryk, R.A.: On the effectiveness of fuzzy clustering as a data discretization technique for large-scale classification of solar images. In: IEEE Int. Conf. on Fuzzy Systems, FUZZ-IEEE, pp. 2019–2024 (August 2009)
Banda, J.M., Angryk, R.A.: Usage of dissimilarity measures and multidimensional scaling for large scale solar data analysis. In: Proc. of the 2010 Conf. on Intelligent Data Understanding, CIDU 2010, pp. 189–203 (October 2010)
Schuh, M.A., Angryk, R.A., Pillai, K.G., Banda, J.M., Martens, P.: A Large-Scale Solar Image Dataset with Labeled Event Regions. In: 20th IEEE Int. Conf. on Image Processing, ICIP 2013 (to appear, 2013)
Banda, J.M., Angryk, R.A., Martens, P.C.: On the surprisingly accurate transfer of image parameters between medical and solar images. In: 18th IEEE Int. Conf. on Image Processing, ICIP 2011, pp. 3669–3672 (September 2011)
Benkhalil, A., Zharkova, V., Zharkov, S., Ipson, S.: Active region detection and verification with the solar feature catalogue. Solar Physics 235, 87–106 (2006)
Gagaudakis, G., Rosin, P.L.: Incorporating shape into histograms for cbir. Pattern Recognition 35(1), 81–91 (2002)
Jing, F., Li, M., Zhang, L.: Learning in region-based image retrieval. In: Bakker, E.M., Lew, M., Huang, T.S., Sebe, N., Zhou, X.S. (eds.) CIVR 2003. LNCS, vol. 2728, pp. 199–204. Springer, Heidelberg (2003)
Kulkarni, S., Verma, B.: Fuzzy logic based texture queries for cbir. In: Proc. of the 5th Int. Conf. on Computational Intelligence and Multimedia Applications, ICCIMA 2003, pp. 223–238. IEEE Computer Society, Washington, DC (2003)
Lei, Z., Fuzong, L., Bo, Z.: A CBIR method based on color-spatial feature. In: Proc. of the IEEE Region 10 Conf., TENCON 1999, vol. 1, pp. 166–169 (1999)
Lin, H.-C., Chiu, C.-Y., Yang, S.-N.: Linstar texture: a fuzzy logic cbir system for textures. In: Proc. of the 9th ACM Int. Conf. on Multimedia, MULTIMEDIA 2001, pp. 499–501. ACM, New York (2001)
Martens, P.C.H., Attrill, G.D.R., Davey, A.R., Engell, A., et al.: Computer vision for the Solar Dynamics Observatory (SDO). Solar Physics 275, 79–113 (2012)
Thumfart, S., Heidl, W., Scharinger, J., Eitzinger, C.: A quantitative evaluation of texture feature robustness and interpolation behaviour. In: Jiang, X., Petkov, N. (eds.) CAIP 2009. LNCS, vol. 5702, pp. 1154–1161. Springer, Heidelberg (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Banda, J.M., Schuh, M.A., Wylie, T., McInerney, P., Angryk, R.A. (2014). When Too Similar Is Bad: A Practical Example of the Solar Dynamics Observatory Content-Based Image-Retrieval System. In: Catania, B., et al. New Trends in Databases and Information Systems. Advances in Intelligent Systems and Computing, vol 241. Springer, Cham. https://doi.org/10.1007/978-3-319-01863-8_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-01863-8_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01862-1
Online ISBN: 978-3-319-01863-8
eBook Packages: EngineeringEngineering (R0)