Learned vs. engineered features for fine-grained classification of aquatic macroinvertebrates
Author | Riabchenko, Ekaterina |
Author | Meissner, Kristian |
Author | Ahmad, Iftikhar |
Author | Iosifidis, Alexandros |
Author | Tirronen ,Ville |
Author | Gabbouj, Moncef |
Author | Kiranyazm, Serkan |
Available date | 2021-09-07T06:16:21Z |
Publication Date | 2016 |
Publication Name | Proceedings - International Conference on Pattern Recognition |
Resource | Scopus |
ISSN | 10514651 |
Abstract | Aquatic macroinvertebrate biomonitoring is an efficient way of assessment of slow and subtle anthropogenic changes and their effect on water quality. It is imperative to have reliable identification and counts of the various taxa occurring in samples as these form the basis for the quality indices used to infer the ecological status of the aquatic ecosystem. In this paper, we try to close the gap between human taxa identification accuracy (typically 90-95% on 30-40 classes of macroinvertebrates) and results of automatic fine-grained classification by introducing a novel technique based on Convolutional Neural Networks (CNN). CNN learns optimal features for macroinvertebrate classification and achieves near human accuracy when tested on 29 macroinvertebrate classes. Moreover, we perform comparative evaluation of the learned features against the hand-crafted features, which have been commonly used in classical approaches, and confirm superiority of the learned deep features over the engineered ones. |
Language | en |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Subject | Aquatic ecosystems Neural networks Pattern recognition Water quality Anthropogenic changes Aquatic macroinvertebrates Classical approach Comparative evaluations Convolutional Neural Networks (CNN) Ecological status Identification accuracy Macroinvertebrates Classification (of information) |
Type | Conference Paper |
Pagination | 2276-2281 |
Volume Number | 0 |
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Electrical Engineering [2555 items ]