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Region-based Querying of Solar Data Using Descriptor Signatures Authors: Juan M. Banda 1, Chang Liu 1 and Rafal A. Angryk 2 1 Montana State University,

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Presentation on theme: "Region-based Querying of Solar Data Using Descriptor Signatures Authors: Juan M. Banda 1, Chang Liu 1 and Rafal A. Angryk 2 1 Montana State University,"— Presentation transcript:

1 Region-based Querying of Solar Data Using Descriptor Signatures Authors: Juan M. Banda 1, Chang Liu 1 and Rafal A. Angryk 2 1 Montana State University, Bozeman, MT 2 Georgia State University, Atlanta, GA 1

2 Introduction In this work, we present our first results of our experiments to prove region-based querying capabilities to our existing Solar Dynamics Observatory (SDO) content-based image- retrieval (CBIR) system 2

3 Motivation Current SDO CBIR (http://cbsir.cs.montana.edu/sdocbir) system returns images based on full-image similarityhttp://cbsir.cs.montana.edu/sdocbir We have too many similar images returned (one image every 10 seconds produces plenty of close matches) In a practical scenario a researcher is only interested in a particular region of the image only Query SDO images from sources of multiple repositories 3

4 Our Approach Use our existing image parameters extracted from the solar images to create a 10-bin descriptor signature per region of interest. Create a testing and training dataset based on HEK (Heliophysics Event Knowledge base) reported events Generate the nearest-neighbor list of each event using different metrics based on the signature descriptors Validate retrieval results with classification algorithms 4

5 Image Parameter Extraction Image data becomes 64x64 cells of ten image parameters each 5

6 Solar Event Dataset Based on the dataset introduced by Schuh et al. [1], we selected the following solar events [1] M.A. Schuh, R.A. Angryk, K.G. Pillai, J.M. Banda, and P.C.H. Martens. A large-scale solar image dataset with labeled event regions. In 20 th IEEE Int. Conf. on Image Processing (ICIP), pages 4349–4353,

7 Descriptor signature generation Find intersecting cells with region of interest. Average all image parameter values individually per region. Descriptor is a 10-bin histogram-like structure with each bin representing the average value of each parameter in the region of interest 7

8 Distance measures and classifiers used 8 Classifiers: Naïve Bayes, C 4.5 and Support Vector Machines

9 Experiments S1 - 1-to-all comparison within the same type of events / wavelength combination S2 - 1-to-all comparison within the same type of events S3 - 1-to-all comparison of all signatures against each other (all events and wavelengths) 9

10 Experiment - S1 In this experiment we expect to identify which wavelength / distance measure combinations are ideal to retrieve certain events, with our signature-based approach, and how this matches with what the FFT modules have determined over the past The main aspect of this scenario is observing how we can improve the separation of one event / wavelength combination by using different distance measures 10

11 Results – S1 (1) 11 Note: On the X and Y axis we have each event arranged by their timestamp from earliest to latest. The diagonal of this plot should always be blue since the distance from each event to itself is zero. Blue indicates more similar and red least similar Dissimilarity plots for wavelength flare (FL) event with: a) Euclidean distance, b) Cosine distance

12 Results – S1 (2) 12 Note: On the X and Y axis we have each event arranged by their timestamp from earliest to latest. The diagonal of this plot should always be blue since the distance from each event to itself is zero. Blue indicates more similar and red least similar Dissimilarity plots for wavelengths (a) 131 and (b) 171 for the flare (FL) event. Both plots represent KLD measure

13 Experiment - S2 Instead of using just one wavelength to find events (as on S1), we now try to identify them in a mixed wavelength scenario Here we want to try to validate our visual results, the FFT module results, or find new and interesting combinations 13

14 Results – S2 14

15 Experiment – S3 Simulating a more realistic retrieval scenario, we combined all wavelengths and events We will not rely on any FFT information for this experiment and then see how our results match or can be used with the previous experiments S1 and S2 15

16 Results – S3 16

17 Independent Evaluation Results based on Classification 17

18 Overall conclusions By following the traditional wavelength / event pairings we get solid retrieval precision (75% for all events on average) We still need to rely on some of our expert knowledge (FFT modules) – particularly for sigmoids With our approach the retrieval calculations can now be done in real time due to the compactness of descriptor signatures 18

19 Future/Current Work Add more different types of events Better class balancing using a larger dataset Deploy functionality on our CBIR system for researchers to use Test the descriptor signature approach in other domains (astrophysics) and with other parameters 19

20 Questions? Thank you for your time! 20


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