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2008
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Weakly supervised learning using proportion-based information: An application to fisheries acoustics

12 years 26 days ago
Weakly supervised learning using proportion-based information: An application to fisheries acoustics
This paper addresses the inference of probabilistic classification models using weakly supervised learning. In contrast to previous work, the use of proportion-based training data is investigated in combination to non-linear claassification models. An application to fisheries acoustics and fish school classification is considered and experiments are reported for synthetic and real datasets. 1 Problem statement and related work Sonar echosounder mounted on oceanographic and fishing vessels provides a remote sensing device for observing and analyzing the spatio-temporal variations of marine ecosystems [6]. For instance, fish stock assessment for pelagic species such as anchovy or herring are carried out from such acoustic surveys in the Bay of Biscay or the North Sea []. Isolated fish, fish schools or other kinds of aggregation such as plankton layers are viewed by echo sounder as areas depicting a significant echo compared to the background (i.e., water). Besides, the responses of diff...
Carla Scalarin, Jacques Masse, Jean-Marc Boucher,
Added 05 Nov 2009
Updated 06 Nov 2009
Type Conference
Year 2008
Where ICPR
Authors Carla Scalarin, Jacques Masse, Jean-Marc Boucher, Paul Cauchy, Riwal Lefort, Ronan Fablet
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