Abstract
Image search engines tend to return a large number of images which the engines consider to be relevant, and such pool of results generally is very large and may be regarded to be effectively inexhaustible. While the images are presented as relevant, it is normally true that many of them are actually irrelevant, and that the distribution of relevant images over the returned results is nonuniform. In this paper, we present time series models, including moving average and exponential smoothing, for predicting Image Search Engines (ISEs) results. We are able to establish models which are able to give good and robust prediction of search performance. In addition, the results of this research can have a direct bearing on search engine design to provide informative guidance to users on the retrieval of relevant images, and allow the users to optimize their strategy in the recovery and discovery of images.
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