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UNIFIED LEARNING PARADIGM FOR IMAGE RETRIEVAL

机译：图像检索的统一学习范例

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Dealing with Relevance feedback (RF) using statistical learning has been a key technique to improve the content-based image retrieval (CBIR) performance. However, there is still a big room to further RF performance since the popular RF methods ignore the cooperation among various learning mechanisms. In this paper, we propose a unified learning paradigm (ULP) that integrates the merits of ensemble learning, semi-supervised learning, active learning and long-term learning into a uniform framework. Concretely, unlabeled examples are exploited to facilitate ensemble learning by helping augment the diversity among the base classifiers, and then, a strong ensemble is used to identify the most informative examples for active learning. In particular, the semantic clues are inferred in the long-term learning setting, which serves as the prior knowledge to validate the effectiveness of the unlabeled examples used by ULP. Finally, a bias-weighting strategy is developed to guide the ensemble of classifiers to pay more attention to the positive examples than the negative ones. An empirical study shows that using multiple learning strategies simultaneously in CBIR is beneficial, and that the proposed scheme is significantly more effective than some existing approaches.
机译：使用统计学习处理相关性反馈（RF）已成为提高基于内容的图像检索（CBIR）性能的关键技术。但是，由于流行的射频方法忽略了各种学习机制之间的合作，因此仍有更大的提升射频性能的空间。在本文中，我们提出了一个统一的学习范式（ULP），它将整体学习，半监督学习，主动学习和长期学习的优点整合到一个统一的框架中。具体而言，利用未标记的示例通过帮助增强基础分类器之间的多样性来促进集成学习，然后，使用强大的集成来识别主动学习的最有用的示例。特别是，在长期学习环境中可以推断出语义线索，这可以作为先验知识来验证ULP使用的未标记示例的有效性。最后，提出了一种偏向加权策略，以指导分类器的集成更多地关注积极的例子而不是消极的例子。一项经验研究表明，在CBIR中同时使用多种学习策略是有益的，并且所提出的方案比某些现有方法要有效得多。

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