Coral reef fish detection and recognition in underwater videos by supervised machine learning: Comparison between Deep Learning and HOG+SVM methods

Villon et al. (2016) Advanced Concepts for Intelligent Vision Systems

http://link.springer.com/chapter/10.1007/978-3-319-48680-2_15

Villon S., Chaumont M., Subsol G., Villéger S., Claverie T., Mouillot D. (2016) Coral Reef Fish Detection and Recognition in Underwater Videos by Supervised Machine Learning: Comparison Between Deep Learning and HOG+SVM Methods. In: 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.

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.

Keywords:

Support Vector Machine Feature Vector Coral Reef Deep Learn Convolutional Neural Network