Regional content-based image retrieval for solar images: Traditional versus modern methods

This work presents an extensive evaluation between conventional (distance-based) and modern (search-engine) information retrieval techniques in the context of finding similar Solar image regions within the Solar Dynamics Observatory (SDO) mission image repository. We compare pre-computed image descriptors (image features) extracted from the SDO mission images in two very different ways: (1) similarity retrieval using multiple distance-based metrics and (2) retrieval using Lucene, a general purpose scalable retrieval engine. By transforming image descriptors into histogram-like signatures and into Lucene-compatible text strings, we are able to effectively evaluate the retrieval capabilities of both methodologies. Using the image descriptors alongside a labeled image dataset, we present an extensive evaluation under the criteria of performance, scalability and retrieval precision of experimental retrieval systems in order to determine which implementation would be ideal for a production level system. In our analysis we performed key transformations to our sample datasets to properly evaluate rotation invariance and scalability. At the end of this work we conclude which technique is the most robust and would yield the best performing system after an extensive experimental evaluation, we also point out the strengths and weaknesses of each approach and theorize on potential improvements.