Kiranyaz, SerkanGabbouj, MoncefPulkkinen, Jenniİnce, TürkerMeissner, Kristian2023-06-162023-06-162010978-1-4244-7994-81522-4880https://doi.org/10.1109/ICIP.2010.5651161https://hdl.handle.net/20.500.14365/1947IEEE International Conference on Image Processing -- SEP 26-29, 2010 -- Hong Kong, PEOPLES R CHINAIn this paper, we focus on advanced classification and data retrieval schemes that are instrumental when processing large taxonomical image datasets. With large number of classes, classification and an efficient retrieval of a particular benthic macroinvertebrate image within a dataset will surely pose a severe problem. To address this, we propose a novel network of evolutionary binary classifiers, which is scalable, dynamically adaptable and highly accurate for the classification and retrieval of large biological species-image datasets. The classification and retrieval results for the macroinvertebrate test data attain taxonomic accuracy that equals and even surpasses that of an average expert. Our findings are encouraging for aquatic biomonitoring where cost intensity of sample analysis currently poses a bottleneck for routine biomonitoring.eninfo:eu-repo/semantics/closedAccessIdentificationConference Object10.1109/ICIP.2010.56511612-s2.0-78651065636