When Too Similar Is Bad: A Practical Example of the Solar Dynamics Observatory Content-Based Image-Retrieval System
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.
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