@incollection{furht_combining_2008,
	title = {Combining {Intra}-{Image} and {Inter}-{Class} {Semantics} for {Image} {Matching}},
	copyright = {©2008 Springer-Verlag},
	isbn = {978-0-387-74724-8 978-0-387-78414-4},
	url = {http://link.springer.com/referenceworkentry/10.1007/978-0-387-78414-4_265},
	abstract = {DefinitionBoth local (intra-image) and global (inter-class) similarities play complementary roles in image matching and ranking, so a simple linear combination scheme has been experimented with significant performance improvement over single image matching schemes.Given an image retrieval system, the information need of a user can be modeled as the posterior probability of the set of relevant images R given an expression of the information need in the form of query specification q and an image x in the current database, P(R{\textbar}q,x). The objective of the system is to return images with high probabilities of relevance to the user.In Query By Example, P(R{\textbar}q,x) depends on the similarity between query q and image x. On the other hand, we note that the set of relevant images R does not exist until a query has been specified. However we can construct prior categories of images Ck, k = 1, 2, …, M as some prototypical instances of R and compute the memberships of q and x to these prior c ...},
	language = {en},
	urldate = {2016-05-03},
	booktitle = {Encyclopedia of {Multimedia}},
	publisher = {Springer US},
	editor = {Furht, Borko},
	year = {2008},
	note = {00000},
	pages = {88--89}
}

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