Abstract
Classification of solar event detections into two classes, of either the same object at a later time or an entirely different object, plays a significant role in multiple hypothesis solar event tracking. Many features for this task are produced when images from multiple wavelengths are used and compounded when multiple image parameters are extracted from each of these observations coming from NASA’s Solar Dynamics Observatory. Furthermore, each different event type may require different sets of features to accurately accomplish this task. A feature selection algorithm is required to identify important features extracted from the available images and that can do so without a high computational cost. This work investigates the use of a simple feature subset selection method based on the ANOVA F-Statistic measure as a means of ranking the extracted image parameters in various wavelengths. We show that the feature subsets that are obtained through selecting the top K features ranked in this manner produce classification results as good or better than more complicated methods based on searching the feature subset space for maximum-relevance and minimum-redundancy. We intend for the results of this work to lay the foundations of future work towards a robust model of appearance to be used in the tracking of solar phenomena.
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References
Aherne, F.J., Thacker, N.A., Rockett, P.I.: The Bhattacharyya metric as an absolute similarity measure for frequency coded data. Kybernetika 34(4), 363–368 (1998). http://eudml.org/doc/33362
Banda, J., Liu, C., Angryk, R.: Region-based querying of solar data using descriptor signatures. In: 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW), pp. 1–7, December 2013. http://dx.doi.org/10.1109/ICDMW.2013.127
Banda, J., Angryk, R.: An experimental evaluation of popular image parameters for monochromatic solar image categorization. In: 2010 Twenty-Third FLAIRS Conference, pp. 380–385, May 2010
Berclaz, J., Fleuret, F., Fua, P.: Multiple object tracking using flow linear programming. In: 2009 Twelfth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS-Winter), pp. 1–8, December 2009. http://dx.doi.org/10.1109/PETS-WINTER.2009.5399488
Ding, C., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. In: Proceedings of the 2003 IEEE Bioinformatics Conference, CSB 2003, pp. 523–528, August 2003. http://dx.doi.org/10.1109/CSB.2003.1227396
Hurlburt, N., Cheung, M., Schrijver, C., Chang, L., Freeland, S., Green, S., Heck, C., Jaffey, A., Kobashi, A., Schiff, D., Serafin, J., Seguin, R., Slater, G., Somani, A., Timmons, R.: Heliophysics event knowledge base for the solar dynamics observatory (sdo) and beyond. In: Chamberlin, P., Thompson, B. (eds.) The Solar Dynamics Observatory, pp. 67–78. Springer, US (2012). http://dx.doi.org/10.1007/978-1-4614-3673-7_5
Kempton, D., Pillai, K., Angryk, R.: Iterative refinement of multiple targets tracking of solar events. In: 2014 IEEE International Conference on Big Data (Big Data), pp. 36–44, October 2014. http://dx.doi.org/10.1109/BigData.2014.7004402
Kempton, D., Angryk, R.: Tracking solar events through iterative refinement. Astron. Comput. 13, 124–135 (2015). http://dx.doi.org/10.1016/j.ascom.2015.10.005
Martens, P., Attrill, G., Davey, A., Engell, A., Farid, S., Grigis, P., Kasper, J., Korreck, K., Saar, S., Savcheva, A., Su, Y., Testa, P., Wills-Davey, M., Bernasconi, P., Raouafi, N.E., Delouille, V., Hochedez, J., Cirtain, J., DeForest, C., Angryk, R., De Moortel, I., Wiegelmann, T., Georgoulis, M., McAteer, R., Timmons, R.: Computer vision for the solar dynamics observatory (sdo). Sol. Phys. 275(1–2), 79–113 (2012). http://dx.doi.org/10.1007/s11207-010-9697-y
McInerney, P., Banda, J., Angryk, R.: On using sift descriptors for image parameter evaluation. In: 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW), pp. 32–39, December 2013. http://dx.doi.org/10.1109/ICDMW.2013.123
Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005). http://dx.doi.org/10.1109/TPAMI.2005.159
Schuh, M., Angryk, R.: Massive labeled solar image data benchmarks for automated feature recognition. In: 2014 IEEE International Conference on Big Data (Big Data), pp. 53–60, October 2014. http://dx.doi.org/10.1109/BigData.2014.7004404
Schuh, M., Angryk, R., Pillai, K., Banda, J., Martens, P.: A large-scale solar image dataset with labeled event regions. In: 2013 20th IEEE International Conference on Image Processing (ICIP), pp. 4349–4353, September 2013. http://dx.doi.org/10.1109/ICIP.2013.6738896
Tamura, H., Mori, S., Yamawaki, T.: Textural features corresponding to visual perception. IEEE Trans. Syst. Man Cybern. B Cybern. 8(6), 460–473 (1978). http://dx.doi.org/10.1109/TSMC.1978.4309999
Verbeeck, C., Delouille, V., Mampaey, B., De Visscher, R.: The spoca-suite: software for extraction, characterization, and tracking of active regions andcoronal holes on euv images. Astron. Astrophys. 561, A29 (2014). http://dx.doi.org/10.1051/0004-6361/201321243
Acknowledgment
This work was supported in part by two NASA Grant Awards (No. NNX11AM13A, and No. NNX15AF39G), and one NSF Grant Award (No. AC1443061).
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Kempton, D.J., Schuh, M.A., Angryk, R.A. (2016). Towards Feature Selection for Appearance Models in Solar Event Tracking. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science(), vol 9693. Springer, Cham. https://doi.org/10.1007/978-3-319-39384-1_8
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