Definition
Using 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.
Examples of 26 semantic support regions.
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References
J.H. Lim and J.S. Jin, “A Structured Learning Framework for Content-based Image Indexing and Visual Query,” Multimedia Systems Journal, 2005.
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© 2008 Springer-Verlag
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(2008). Semantic Consumer Image Indexing. In: Furht, B. (eds) Encyclopedia of Multimedia. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-78414-4_209
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DOI: https://doi.org/10.1007/978-0-387-78414-4_209
Publisher Name: Springer, Boston, MA
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