基于内容的图像检索的层次聚类关联反馈

Ionut Mironica, B. Ionescu, C. Vertan
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引用次数: 4

摘要

在本文中,我们讨论了基于内容的图像检索中的相关反馈问题。我们提出了一种方法,在查询中使用相关和不相关图像的分层聚类表示。这种策略的主要优点是在检索图像的初始集上执行(对于少量检索图像只提供一次用户反馈),而不是像大多数方法那样执行额外的查询。在几个标准图像数据库上进行的实验测试和使用最先进的内容描述符(例如MPEG-7, SURF)表明,所提出的方法在检索性能方面有显著改善,优于其他一些经典方法。
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Hierarchical clustering relevance feedback for content-based image retrieval
In this paper we address the issue of relevance feedback in the context of content-based image retrieval. We propose a method that uses an hierarchical cluster representation of the relevant and non-relevant images in a query. The main advantage of this strategy is in performing on the initial set of the retrieved images (user feedback is provided only once for a small number of retrieved images) instead of performing additional queries as most approaches do. Experimental tests conducted on several standard image databases and using state-of-the-art content descriptors (e.g. MPEG-7, SURF) show that the proposed method provides a significant improvement in the retrieval performance, outperforming some other classic approaches.
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