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Learning to Rerank Web Images
April-June 2013 (vol. 20 no. 2)
pp. 13-21
Linjun Yang, Microsoft
Alan Hanjalic, Delft University of Technology, the Netherlands
This article reviews recent advancements in developing approaches to Web image search reranking. The authors provide a categorization of related theories and algorithms and include a mathematical formulation, analysis, and discussion per category. They highlight the limitations of the existing approaches and make recommendations on what they believe to be the most critical research directions to improve the efficiency, effectiveness, and overall utility of Web image search reranking technology.
Index Terms:
Search engines,Visualization,Search problems,Search methods,Mathematical model,Algorithm design and analysis,Information retrieval,search reranking,Search engines,Visualization,Search problems,Search methods,Mathematical model,Algorithm design and analysis,Information retrieval,search engine architecture,multimedia,multimedia applications,Web technology,image search
Citation:
Linjun Yang, Alan Hanjalic, "Learning to Rerank Web Images," IEEE Multimedia, vol. 20, no. 2, pp. 13-21, April-June 2013, doi:10.1109/MMUL.2012.30
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