NDLI: Context awareness in graph-based image semantic segmentation via visual word distributions

Content Provider	IEEE Xplore Digital Library
Author	Passino, G. Patras, I. Izquierdo, E.
Copyright Year	2009
Description	Author affiliation: Queen Mary University of London, Mile End Road, E1 4NS, UK (Passino, G.; Patras, I.; Izquierdo, E.)
Abstract	This paper addresses the problem of image semantic segmentation (or semantic labelling), that is the association of one of a predefined set of semantic categories (e.g. cow, car, face) to each image pixel. We adopt a patch-based approach, in which super-pixel elements are obtained via oversegmentation of the original image. We then train a Conditional Random Field on heterogeneous descriptors extracted at different scales and locations. This discriminative graphical model can effectively account for the statistical dependence of neighbouring patches. For the more challenging task of considering long-range patch dependency and contextualisation, we propose the use of a descriptor based on histograms of visual words extracted in the vicinity of each patch at different scales. Experiments validate our approach by showing improvements with respect to both a base model not using distributed features and the state of the art works in the area.
Starting Page	33
Ending Page	36
File Size	379519
Page Count	4
File Format	PDF
ISBN	9781424436095
e-ISBN	9781424436101
DOI	10.1109/WIAMIS.2009.5031425
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-06
Publisher Place	United Kingdom
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Histograms Graphical models Layout Context awareness Object detection Labeling Face detection Pixel Context modeling
Content Type	Text
Resource Type	Article

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