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 fish behavior. 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 an 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 computes 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.
C. Spampianto et al. REF attempt to classify fishes using texture features extracted from the gabor filtering, gray level histogram and the histogram of Fourier descriptors of boundaries and Curvature Scale Space Transform used for shape features extracted using.
Automatic fish classification for underwater species behavior understanding
ARTEMIS '10
Computer Science