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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... |
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| Ano: 2010 |
URL: http://archimer.ifremer.fr/doc/00030/14119/11371.pdf |
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| Lefort, Riwal; Fablet, Ronan; Berger, Laurent; Boucher, Jm. |
| In this letter, we address the characterization of objects in 3-D sonar images of the water column obtained by a multibeam echo sounder. Compared with classic 2-D images from a monobeam echo sounder, these 3-D images provide finer scale observation of the pelagic biomasses and new tools to characterize 3-D distributions. By viewing object patterns as realizations of spatial point processes, we investigate descriptive spatial statistics. This method is then applied to 3-D fisheries acoustics data set for characterization of the distribution of pelagic fish schools. Reported experiments illustrate the relevance of the proposed descriptors. The comparison of our method with 2-D sonar data analysis further demonstrates the information gain from using 3-D sonar... |
| Tipo: Text |
Palavras-chave: Fisheries acoustics; Multibeam sensor; Object patterns in images; Point processes; Spatial statistics. |
| Ano: 2012 |
URL: http://archimer.ifremer.fr/doc/00056/16769/14270.pdf |
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| Lefort, Riwal; Fablet, Ronan; Boucher, I-m. |
| This paper addresses the inference of probabilistic classification models using weakly supervised learning The main contribution of this work is the development of learning methods for training datasets consisting of groups of objects with known relative class priors This can be regarded as a generalization of the situation addressed by Bishop and Ulusoy (2005) where training information is given as the presence or absence of object classes in each set Generative and discriminative classification methods are conceived and compared for weakly supervised learning as well as a non-linear version of the probabilistic discriminative models The considered models are evaluated on standard datasets and an application to fisheries acoustics is reported The proposed... |
| Tipo: Text |
Palavras-chave: Weakly supervised learning; Generative classification model; Discriminative classification model. |
| Ano: 2011 |
URL: http://archimer.ifremer.fr/doc/00030/14103/11372.pdf |
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