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.
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Submitted on : Thursday, September 29, 2016 - 5:31:00 PM
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Sébastien Villon, Marc Chaumont, Gérard Subsol, Sébastien Villéger, Thomas Claverie, et al.. Coral reef fish detection and recognition in underwater videos by supervised machine learning : Comparison between Deep Learning and HOG+SVM methods. ACIVS: Advanced Concepts for Intelligent Vision Systems, Oct 2016, Lecce, Italy. ⟨hal-01374123⟩

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