Semantic Class-Based Image Indexing. Furht, B., editor In Encyclopedia of Multimedia, pages 799–799. Springer US, 2008. 00000
Semantic Class-Based Image Indexing [link]Paper  abstract   bibtex   
DefinitionIn a unique Class Relative Indexing (CRI) scheme, image classification is a means to compute inter-class semantic image indexes for similarity-based matching and retrieval.A natural and useful insight is to formulate image retrieval as a classification problem. In very general terms, the goal of image retrieval is to return images of a class C that the user has in mind based on a set of features computed for each image x in the database. In probabilistic sense, the system should return images ranked in the descending return status value of P(C\textbarx), whatever C may be defined as desirable. For example, a Bayesian formulation to minimize the probability of retrieval error (i.e., the probability of wrong classification) had been proposed [1] to drive the selection of color and texture features and to unify similarity measures with the maximum likelihood criteria.Image classification or class-based retrieval approaches are adequate for query by predefined image class. How ...
@incollection{furht_semantic_2008-1,
	title = {Semantic {Class}-{Based} {Image} {Indexing}},
	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_208},
	abstract = {DefinitionIn a unique Class Relative Indexing (CRI) scheme, image classification is a means to compute inter-class semantic image indexes for similarity-based matching and retrieval.A natural and useful insight is to formulate image retrieval as a classification problem. In very general terms, the goal of image retrieval is to return images of a class C that the user has in mind based on a set of features computed for each image x in the database. In probabilistic sense, the system should return images ranked in the descending return status value of P(C{\textbar}x), whatever C may be defined as desirable. For example, a Bayesian formulation to minimize the probability of retrieval error (i.e., the probability of wrong classification) had been proposed [1] to drive the selection of color and texture features and to unify similarity measures with the maximum likelihood criteria.Image classification or class-based retrieval approaches are adequate for query by predefined image class. How ...},
	language = {en},
	urldate = {2016-05-03},
	booktitle = {Encyclopedia of {Multimedia}},
	publisher = {Springer US},
	editor = {Furht, Borko},
	year = {2008},
	note = {00000},
	pages = {799--799}
}
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