Plant Recognition by Inception Networks with Test-time Class Prior Estimation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F18%3A43952556" target="_blank" >RIV/49777513:23520/18:43952556 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21230/18:00322496
Result on the web
<a href="http://ceur-ws.org/Vol-2125/paper_152.pdf" target="_blank" >http://ceur-ws.org/Vol-2125/paper_152.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Plant Recognition by Inception Networks with Test-time Class Prior Estimation
Original language description
The paper describes an automatic system for recognition of 10,000 plant species from one or more images. The system finished 1st in the ExpertLifeCLEF 2018 plant identification challenge with 88.4% accuracy and performed better than 5 of the 9 participating plant identification experts. The system is based on the Inception-ResNet-v2 and Inception-v4 Convolutional Neural Network (CNN) architectures. Performance improvements were achieved by: adjusting the CNN predictions according to the estimated change of the class prior probabilities, replacing network parameters with their running averages, and test-time data augmentation.
Czech name
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Czech description
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Project
<a href="/en/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Center for Large Scale Multi-modal Data Interpretation</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Working Notes of CLEF 2018 - Conference and Labs of the Evaluation Forum
ISBN
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ISSN
1613-0073
e-ISSN
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Number of pages
8
Pages from-to
8
Publisher name
CEUR-WS
Place of publication
Aachen
Event location
Avignon, France
Event date
Sep 10, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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