| Registro completo |
| Provedor de dados: |
ArchiMer
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| País: |
France
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| Título: |
Coral Reef Fish Detection and Recognition in Underwater Videos by Supervised Machine Learning: Comparison Between Deep Learning and HOG plus SVM Methods
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| Autores: |
Villon, Sebastien
Chaumont, Marc
Subsol, Gerard
Villeger, Sebastien
Claverie, Thomas
Mouillot, David
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| Data: |
2016
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| Ano: |
2016
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| Palavras-chave: |
Support Vector Machine
Feature Vector
Coral Reef
Deep Learn
Convolutional Neural Network
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| Resumo: |
In this paper, we present two supervised machine learning methods to automatically detect and recognize coral reef fishes in underwater HD videos. The first method relies on a traditional two-step approach: extraction of HOG features and use of a SVM classifier. The second method is based on Deep Learning. We compare the results of the two methods on real data and discuss their strengths and weaknesses.
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| Tipo: |
Text
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| Idioma: |
Inglês
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| Identificador: |
https://archimer.ifremer.fr/doc/00387/49860/74458.pdf
DOI:10.1007/978-3-319-48680-2_15
https://archimer.ifremer.fr/doc/00387/49860/
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| Editor: |
Blanc-Talon J., Distante C., Philips W., Popescu D., Scheunders P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2016.Lecture Notes in Computer Science, vol 10016. Springer, Cham. pp.160-171
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| Formato: |
application/pdf
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| Direitos: |
info:eu-repo/semantics/openAccess
restricted use
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