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Information-Theoretic Active Learning for Content-Based Image Retrieval

2018-09-07 07:57:26
Björn Barz, Christoph Käding, Joachim Denzler

Abstract

We propose Information-Theoretic Active Learning (ITAL), a novel batch-mode active learning method for binary classification, and apply it for acquiring meaningful user feedback in the context of content-based image retrieval. Instead of combining different heuristics such as uncertainty, diversity, or density, our method is based on maximizing the mutual information between the predicted relevance of the images and the expected user feedback regarding the selected batch. We propose suitable approximations to this computationally demanding problem and also integrate an explicit model of user behavior that accounts for possible incorrect labels and unnameable instances. Furthermore, our approach does not only take the structure of the data but also the expected model output change caused by the user feedback into account. In contrast to other methods, ITAL turns out to be highly flexible and provides state-of-the-art performance across various datasets, such as MIRFLICKR and ImageNet.

Abstract (translated)

我们提出了信息理论主动学习(ITAL),一种用于二进制分类的新型批处理模式主动学习方法,并将其应用于在基于内容的图像检索的上下文中获取有意义的用户反馈。我们的方法不是结合不同的启发式方法,例如不确定性,多样性或密度,而是基于最大化图像的预测相关性和关于所选批次的预期用户反馈之间的互信息。我们提出了对这个计算要求严格的问题的适当近似,并且还集成了用户行为的显式模型,该模型考虑了可能的错误标签和不可命名的实例。此外,我们的方法不仅考虑了数据的结构,还考虑了用户反馈引起的预期模型输出变化。与其他方法相比,ITAL具有高度灵活性,可在各种数据集中提供最先进的性能,例如MIRFLICKR和ImageNet。

URL

https://arxiv.org/abs/1809.02337

PDF

https://arxiv.org/pdf/1809.02337.pdf


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