Abstract
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.
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Huiskes, M.J. (2006). Image Searching and Browsing by Active Aspect-Based Relevance Learning. In: Sundaram, H., Naphade, M., Smith, J.R., Rui, Y. (eds) Image and Video Retrieval. CIVR 2006. Lecture Notes in Computer Science, vol 4071. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11788034_22
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DOI: https://doi.org/10.1007/11788034_22
Publisher Name: Springer, Berlin, Heidelberg
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