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TUTCRIS

Data Enrichment in Fine-Grained Classification of Aquatic Macroinvertebrates

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2016 ICPR 2nd Workshop on Computer Vision for Analysis of Underwater Imagery (CVAUI)
KustantajaIEEE
Sivut43-48
Sivumäärä6
ISBN (elektroninen)978-1-5090-5870-9
DOI - pysyväislinkit
TilaJulkaistu - 4 joulukuuta 2016
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaICPR Workshop on Computer Vision for Analysis of Underwater Imagery -
Kesto: 1 tammikuuta 1900 → …

Conference

ConferenceICPR Workshop on Computer Vision for Analysis of Underwater Imagery
Ajanjakso1/01/00 → …

Tiivistelmä

The types and numbers of benthic macroinvertebrates found in a water body reflect water quality. Therefore, macroinvertebrates are routinely monitored as a part of freshwater ecological quality assessment. The collected macroinvertebrate samples are identified by human experts, which is costly and time-consuming. Thus, developing automated identification methods that could partially replace the human effort is important. In our group, we have been working toward this goal and, in this paper, we improve our earlier results on automated macroinvertebrate classification obtained using deep Convolutional Neural Networks (CNNs). We apply simple data enrichment prior to CNN training. By rotations and mirroring, we create new images so as to increase the total size of the image database sixfold. We evaluate the effect of data enrichment on Caffe and MatConvNet CNN implementations. The networks are trained either fully on the macroinvertebrate data or first pretrained using ImageNet pictures and then fine-tuned using the macroinvertebrate data. The results show 3-6% improvement, when the enriched data are used. This is an encouraging result, because it significantly narrows the gap between automated techniques and human experts, while it leaves room for future improvements as even the size of the enriched data, about 60000 images, is small compared to data sizes typically required for efficient training of deep CNNs.

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