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  • @article{MPS2004,
    	vgclass =	{refpap},
    	vgproject =	{viper,cbir},
    	author =	{Henning M\"uller and Thierry Pun and David McG.\ Squire},
    	title =	{Learning from user behaviour in image retrieval:
    	{A}pplication of market basket analysis},
    	journal =	{International Journal of Computer Vision},
    	volume =	{56},
    	number =	{1--2},
    	pages =	{65--77},
    	year =	{2004},
    	note =	{(Special Issue on Content-Based Image Retrieval)},
    	doi =	{},
    	abstract =	{This article describes an approach to learn feature
    	weights for content-based image retrieval (CBIR) from user interaction
    	log files. These usage log files are analyzed for images marked
    	together in the same query step. The problem is somewhat similar to one
    	of the traditional data mining problems, the market basket analysis
    	problem, where items bought together in a supermarket are analyzed.
    	This paper outlines similarities and differences between the two fields
    	and explains how to use the interaction data for deriving a better
    	feature weighting.  Experiments with existing log files are done and a
    	significant improvement in performance is reached with a feature
    	weighting calculated from the information contained in the log files.
    	Even with several steps of relevance feedback the results remain much
    	better than without the learning, which means that not only information
    	from feedback is taken into account earlier, but a better quality of
    	retrieval is reached in all steps.},