@incollection{furht_semantic_2008-2,
	title = {Semantic {Consumer} {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_209},
	abstract = {DefinitionUsing Semantic Support Regions, the indexing process automatically detects the layout and applies the right tessellation template.As a structured learning approach to represent and index consumer images with Semantic Support Regions (SSRs) (see article on Semantic Image Representation and Indexing), 26 SSRs have been designed and organized into eight super-classes (Fig. 1) from a collection of 2,400 unconstrained consumer images, taken over 5 years in several countries with indoor/outdoor settings, portrait/landscape layouts, and bad quality images (faded, over-/under-exposed, blurred etc). After removing noisy marginal pixels, the images are resized to 240 × 360. The indexing process automatically detects the layout and applies the right tessellation template.Semantic Consumer Image Indexing. Figure 1.Examples of 26 semantic support regions.A total of 554 image regions from 138 images are cropped and 375 of them are used as training data for Support Vector Machines ...},
	language = {en},
	urldate = {2016-05-03},
	booktitle = {Encyclopedia of {Multimedia}},
	publisher = {Springer US},
	editor = {Furht, Borko},
	year = {2008},
	note = {00000},
	pages = {799--800}
}

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