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| Lefort, Riwal; Fablet, Ronan; Boucher, Jean-marc. |
| 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 introduced and discussed, and an application to fishenes acoustics is presented. Experiments show that random forests outperform discriminative and generative models in supervised learning but random forests are not robust to high complexity class... |
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| Ano: 2011 |
URL: http://archimer.ifremer.fr/doc/00077/18782/16489.pdf |
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| Lefort, Riwal; Fablet, Ronan; Boucher, Jean-marc. |
| The development of robust classification model is among the important issues in computer vision. This paper deals with weakly supervised learning that generalizes the supervised and semi-supervised learning. In weakly supervised learning training data are given as the priors of each class for each sample. We first propose a weakly supervised strategy for learning soft decision trees. Besides, the introduction of elms priors for training samples instead of hard class labels makes natural the formulation of an iterative learning procedure. We report experiments for UCI object recognition datasets. These experiments show that recognition performance close to the supervised learning can be expected using the propose framework. Besides, an application to... |
| Tipo: Text |
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| Ano: 2010 |
URL: http://archimer.ifremer.fr/doc/00030/14119/11371.pdf |
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| Le Chenadec, Gilles; Boucher, Jean-marc; Lurton, Xavier. |
| Backscattered signal statistics are widely used for target detection and seafloor characterization. The K-distribution shows interesting properties for describing experimental backscattered intensity statistics. In addition to the fact that its probability distribution function accurately fits actual sonar data, it advantageously provides a physical interpretation linked to the backscattering phenomenon. Sonar systems usually record backscattered signals from a wide angular range across the ship's track. In this context, previous studies have shown that backscatter statistics strongly depend on the incidence angle. In this paper, we propose an extension of previous works to model the angular evolution of the K-distribution shape parameter. This evolution... |
| Tipo: Text |
Palavras-chave: K distribution; Sonar statistical analysis; Seafloor classification. |
| Ano: 2007 |
URL: http://archimer.ifremer.fr/doc/2007/publication-2599.pdf |
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