Automatic fish classification for underwater species behavior understanding

Authors:
Concetto Spampinato;Daniela Giordano;Roberto Di Salvo;Yun-Heh Jessica Chen-Burger;Robert Bob Fisher;Gayathri Nadarajan
Affiliations:
University of Catania, Catania, Italy;University of Catania, Catania, Italy;University of Catabua, Catania, Italy;University of Edinburgh, Edinburgh, Scotland Uk;University of Edinburgh, Edinburgh, Scotland Uk;University of Edinburgh, Edinburgh, Scotland Uk
Venue:
Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams
Year:
2010

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Abstract

The aim of this work is to propose an automatic fish classification system that operates in the natural underwater environment to assist marine biologists in understanding subehavior. Fish classification is performed by combining two types of features: 1) Texture features extracted by using statistical moments of the gray-level histogram, spatial Gabor filtering and properties of the co-occurrence matrix and 2) Shape Features extracted by using the Curvature Scale Space transform and the histogram of Fourier descriptors of boundaries. An affine transformation is also applied to the acquired images to represent fish in 3D by multiple views for the feature extraction. The system was tested on a database containing 360 images of ten different species achieving as average correct rate of about 92%. Then, fish trajectories extracted using the proposed fish classification combined with a tracking system, are analyzed in order to understand anomalous behavior. In detail, the tracking layer computer fish trajectories, the classification layer associates trajectories to fish species and then by clustering these trajectories we are able to detect unusual fish behaviors to be further investigated by marine biologists.