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Weakly Supervised Learning: Application to Fish School Recognition

机译：弱监督学习：在鱼类学校识别中的应用

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摘要

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 fisheries 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 proportions. Finally, a compromise is achieved by taking a combination of classifiers that keeps the accuracy of random forests and exploits the robustness of discriminative models.
机译：本章涉及涉及弱监督分类模型的图像中的对象识别。在弱监督学习中，训练数据集的标签信息是每个课程的先验知识。这种先验知识来自图像的全局比例注释。在本章中，我们在弱监督分类问题中比较了三种相对的分类模型：生成模型，判别模型和基于随机森林的模型。首先介绍和讨论模型，然后介绍其在渔业声学中的应用。实验表明，在有监督的学习中，随机森林的性能优于区分模型和生成模型，但对于高复杂性类别比例，随机森林并不稳健。最后，通过采用分类器的组合来达成妥协，该分类器可保持随机森林的准确性并利用判别模型的鲁棒性。

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