Fish Species Recognition from Video using SVM Classifier
Katy Blanc, Diane Lingrand, Fŕed́eric Precioso · 2014
To build a detailed knowledge of the biodiversity, the geographical distribution and the evolution of the alive species is essential for a sustainable development and the preservation of this biodiversity. Massive databases of underwater video surveillance have been recently made available for supporting designing algorithms targeting the identification of fishes. However these video datasets are rather poor in terms of video resolution, pretty challenging regarding both the natural phenomena to be considered such as murky water, seaweed moving the water current, etc, and the huge amount of data to be processed. We have designed a processing chain based on background segmentation, selection keypoints with an adaptive scale, description with OpponentSift and learning of each species by a binary linear Support Vector Machines classifier.