Browsing by Author "Meissner, Kristian"

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Citation - WoS: 5
Citation - Scopus: 5
The Effect of Automated Taxa Identification Errors on Biological Indices
(Pergamon-Elsevier Science Ltd, 2017) Arje, Johanna; Karkkainen, Salme; Meissner, Kristian; Iosifidis, Alexandros; İnce, Türker; Gabbouj, Moncef; Kiranyaz, Serkan
In benthic macroinvertebrate biomonitoring systems, the target is to determine the status of ecosystems based on several biological indices. To increase cost-efficiency, computer-based taxa identification for image data has recently been developed. Taxa identification errors can, however, have strong effects on the indices and thus on the determination of the ecological status. In order to shift the biomonitoring process towards automated expert systems, we need a clear understanding on the bias caused by automation. In this paper, we examine eleven classification methods in the case of macroinvertebrate image data and show how their classification errors propagate into different biological indices. We evaluate 14 richness, diversity, dominance and similarity indices commonly used in biomonitoring. Besides the error rate of the classification method, we discuss the potential effect of different types of identification errors. Finally, we provide recommendations on indices that are least affected by the automatic identification errors and could be used in automated biomonitoring. (C) 2016 Elsevier Ltd. All rights reserved.
Citation - WoS: 11
Citation - Scopus: 21
Network of Evolutionary Binary Classifiers for Classification and Retrieval in Macroinvertebrate Databases
(IEEE, 2010) Kiranyaz, Serkan; Gabbouj, Moncef; Pulkkinen, Jenni; İnce, Türker; Meissner, Kristian
In 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.