Paper Info
Title
Illumination Estimation Based on Bilayer Sparse Coding
Abstract
Computational color constancy is a very important topic in computer vision and has attracted many researchers' attention. Recently, lots of research has shown the effects of using high level visual content cues for improving illumination estimation. However, nearly all the existing methods are essentially combinational strategies in which image's content analysis is only used to guide the combination or selection from a variety of individual illumination estimation methods. In this paper, we propose a novel bilayer sparse coding model for illumination estimation that considers image similarity in terms of both low level color distribution and high level image scene content simultaneously. For the purpose, the image's scene content information is integrated with its color distribution to obtain optimal illumination estimation model. The experimental results on real-world image sets show that our algorithm is superior to some prevailing illumination estimation methods, even better than some combinational methods.
Year
DOI
Venue
2013
10.1109/CVPR.2013.187
CVPR
Keywords
Field
DocType
image coding,image similarity,individual illumination estimation method,content analysis,image content analysis,color constancy,visual content cue,sparse coding,bilayer sparse coding,illumination estimation,scene content information,computer vision,optimal illumination estimation model,prevailing illumination estimation method,real-world image set,high level image scene,computational color constancy,image colour analysis,encoding,lighting,image reconstruction,estimation,vectors
HSL and HSV,Color constancy,Computer vision,Pattern recognition,Neural coding,Computer science,Image coding,Artificial intelligence,Color normalization,Bilayer,Color image
Conference
Volume
Issue
ISSN
2013
1
1063-6919
Citations 
PageRank 
References 
4
0.40
18
Authors
4
Name
Order
Citations
PageRank
Bing Li123411.37
Weihua Xiong229617.96
Weiming Hu35300261.38
Houwen Peng41838.74