By Topic

Aspect coherence for graph-based image labelling

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $31
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Passino, Giuseppe ; Queen Mary, University of London, Mile End Road, E1 4NS, United Kingdom ; Patras, Ioannis ; Izquierdo, Ebroul

Semantic image labelling is the task of assigning each pixel of an image to a semantic category. To this end, in low-level image labelling, a labelled training set is available. In such a situation, structural information about the correlation between different image parts is particularly important. When a part-based inference algorithm is used to perform the association of semantic classes to pixels, however, a good choice on how to use structural information is crucial for learning an efficient and generalisable probabilistic model for the labelling task. In this paper we introduce an efficient way to take into account correlation between different image parts, embedding the parts relationships in a graph built according to aspect coherence of neighbouring image patches.

Published in:

Visual Information Engineering, 2008. VIE 2008. 5th International Conference on

Date of Conference:

July 29 2008-Aug. 1 2008