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TUTCRIS

On Confidences and Their Use in (Semi-)Automatic Multi-Image Taxa Identification

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
KustantajaIEEE
Sivut1338-1343
Sivumäärä6
ISBN (elektroninen)9781728124858
ISBN (painettu)978-1-7281-2486-5
DOI - pysyväislinkit
TilaJulkaistu - 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE Symposium Series on Computational Intelligence -
Kesto: 1 tammikuuta 1900 → …

Conference

ConferenceIEEE Symposium Series on Computational Intelligence
LyhennettäIEEE SSCI
Ajanjakso1/01/00 → …

Tiivistelmä

We analyzed classification confidences in biological multi-image taxa identification problems, where each specimen is represented by multiple images. We observed that confidences can be exploited to progress toward semi-automated identification process, where images are initially classified using a convolutional neural network and taxonomic experts manually inspect only the samples with a low confidence. We studied different ways to evaluate confidences and concluded that the difference of the largest and second largest values in unnormalized network outputs leads to best results. Furthermore, we compared different ways to use image-wise confidences when deciding on the final identification using all the input images of a specimen. The best results were obtained using a confidence-weighted sum rule over the unnormalized outputs. This approach also outperformed the evaluated supervised decision method.