2011
DOI: 10.1007/978-3-642-11739-8_10
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Abstract: This chapter deals with object recognition in images involving a weakly supervised classification model. In weakly supervised learning, the label information of the training dataset is provided as a prior knowledge for each class. This prior knowledge is coming from a global proportion annotation of images. In this chapter, we compare three opposed classification models in a weakly supervised classification issue: a generative model, a discriminative model and a model based on random forests. Models are first … Show more

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Cited by 1 publication
(1 citation statement)
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“…Computerized systems for fish school detection and sizing came into major use with the onset of the computer technology era in the mid-1970s [5], [6]. Classification algorithms were already applied for species identification from hydroacoustic data [7]. Recently the random forest classification algorithm [8] was applied for the identification of Thunnus thynnus (Atlantic bluefin tuna) from sonar images in the region of the Bay of Biscay [9].…”
Section: Introductionmentioning
confidence: 99%
“…Computerized systems for fish school detection and sizing came into major use with the onset of the computer technology era in the mid-1970s [5], [6]. Classification algorithms were already applied for species identification from hydroacoustic data [7]. Recently the random forest classification algorithm [8] was applied for the identification of Thunnus thynnus (Atlantic bluefin tuna) from sonar images in the region of the Bay of Biscay [9].…”
Section: Introductionmentioning
confidence: 99%