Abstract : To build a detailed knowledge of the biodiversity, the geo-
graphical distribution and the evolution of the alive species
is essential for a sustainable development and the preser-
vation of this biodiversity. Massive databases of underwa-
ter 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 wa-
ter, 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.
Our algorithm has been evaluated in the context of our
participation to the Fish task of the LifeCLEF2014 chal-
lenge. Compared to the baseline designed by the LifeCLEF
challenge organizers, our approach reaches a better precision
but a worse recall. Our performances in terms of species
recognition (based only on the correctly detected bounding
boxes) is comparable to the baseline, but our bounding boxes
are often too large and our score is so penalized. Our results
are thus really encouraging.