Combining Intra-Image and Inter-Class Semantics for Image Matching. Furht, B., editor In Encyclopedia of Multimedia, pages 88–89. Springer US, 2008. 00000
Combining Intra-Image and Inter-Class Semantics for Image Matching [link]Paper  abstract   bibtex   
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\textbarq,x). The objective of the system is to return images with high probabilities of relevance to the user.In Query By Example, P(R\textbarq,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 ...
@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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