Bags-of-Features for fish school cluster characterization in pelagic ecosystems:
application to the discrimination of juvenile and adult anchovy clusters off Peru
Ronan Fablet, Paul Gay, Salvador Peraltilla, Cecilia Peña, Ramiro Castillo, Arnaud
Bertrand.
Fisheries acoustics data presents a great potential for the characterization of pelagic
ecosystems, and especially the spatial distribution of pelagic fish. Whereas previous
work has mainly focused on the detection, characterization and recognition of individual
fish schools, we here addressed the characterization and discrimination of fish school
clusters. The proposed scheme relied on the application to acoustic echograms of the
Bags-of-Features approach. The latter is widely exploited for pattern recognition issues
and relies on extraction and categorization of objects in images; the image descriptor
being formed by the count of objects in each object category. This approach is
particularly suited to fisheries acoustic data where fish schools appear as a natural and
meaningful object concept. We applied this approach to the discrimination of juvenile
and adult anchovy clusters off Peru. Echogram-level discrimination performance raised
between 88% and 92% of correct classification for different survey datasets. Significant
improvement (about 10% of correct classification) was reported compared to previously
proposed school-based echogram-level characteristics. The proposed school cluster
classification model was applied to the mapping of juvenile and adult anchovy
population from routine acoustic survey data