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

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.


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