Image Searching and Browsing by Active Aspect-Based Relevance Learning

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Aspect-based relevance learning is a relevance feedback scheme based on a natural model of relevance in terms of image aspects. In this paper we propose a number of active learning and interaction strategies, capitalizing on the transparency of the aspect-based framework. Additionally, we demonstrate that, relative to other schemes, aspect-based relevance learning upholds its retrieval performance well under feedback consisting mainly of example images that are only partially relevant.