2016/12/07 by Parmita Mehta, Sven Dorkenwald, Mehta, Parmita +20 · 1 voice
Computer Science · Decision Sciences · Medicine · #Advanced Image and Video Retrieval Techniques #Databases (cs.DB) #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Retinal Imaging and Analysis #Scientific Computing and Data Management #cs.DB
paper · pdf · doi:10.48550/arxiv.1612.02485
openalex publication_date 2016/12/07 · arxiv published 2016/12/07 · arxiv updated 2016/12/07 · openalex created_date 2022/10/02 · openalex updated_date 2026/08/04
Scientific discoveries are increasingly driven by analyzing large volumes of image data. Many new libraries and specialized database management systems (DBMSs) have emerged to support such tasks. It is unclear, however, how well these systems support real-world image analysis use cases, and how performant are the image analytics tasks implemented on top of such systems. In this paper, we present the first comprehensive evaluation of large-scale image analysis systems using two real-world scientific image data processing use cases. We evaluate five representative systems (SciDB, Myria, Spark, Dask, and TensorFlow) and find that each of them has shortcomings that complicate implementation or hurt performance. Such shortcomings lead to new research opportunities in making large-scale image analysis both efficient and easy to use.