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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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Coral Reef Fish Detection and Recognition in Underwater Videos by Supervised Machine Learning: Comparison Between Deep Learning and HOG plus SVM Methods

Sebastien Villon, Marc Chaumont, Gerard Subsol, Sebastien Villeger, Thomas Claverie et David Mouillot
ADVANCED CONCEPTS FOR INTELLIGENT VISION SYSTEMS, ACIVS 2016, Vol.10016, pp.160-171
Lecture Notes in Computer Science
01/01/2016
DOI: https://doi.org/10.1007/978-3-319-48680-2_15

Résumé

Computer Science Computer Science, Artificial Intelligence Computer Science, Theory & Methods Science & Technology Technology
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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