Name
Title | ||
|---|---|---|
Coral Reef Fish Detection and Recognition in Underwater Videos by Supervised Machine Learning: Comparison Between Deep Learning and HOG+SVM Methods. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-48680-2_15 | ADVANCED CONCEPTS FOR INTELLIGENT VISION SYSTEMS, ACIVS 2016 |
Field | DocType | Volume |
Feature vector,Pattern recognition,Computer science,Convolutional neural network,Support vector machine,Coral reef,Artificial intelligence,Deep learning,Coral reef fish,Strengths and weaknesses,Machine learning,Underwater | Conference | 10016 |
ISSN | Citations | PageRank |
0302-9743 | 5 | 0.57 |
References | Authors | |
10 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sébastien Villon | 1 | 5 | 0.57 |
| Marc Chaumont | 2 | 172 | 20.40 |
| Gérard Subsol | 3 | 393 | 84.30 |
| Sébastien Villéger | 4 | 7 | 1.29 |
| Thomas Claverie | 5 | 7 | 0.95 |
| David Mouillot | 6 | 7 | 0.95 |