Document Detail

Learning Templates for Artistic Portrait Lighting Analysis.
MedLine Citation:
PMID:  25398179     Owner:  NLM     Status:  Publisher    
Lighting is a key factor in creating impressive artistic portraits. In this paper, we propose to analyse portrait lighting by learning templates of lighting styles. Inspired by the experience of artists, we first define several novel features which describe the local contrasts in various face regions. The most informative features are then selected with a stepwise feature pursuit algorithm to derive the templates of various lighting styles. After that, the matching scores which measure the similarity between a testing portrait and those templates are calculated for lighting style classification. Furthermore, we train a regression model by the subjective scores and the feature responses of a template to predict the score of a portrait lighting quality. Based on the templates, a novel Face Illumination Descriptor (FID) is defined to measure the difference between two portrait lightings. Experimental results show that the learned templates can well describe the lighting styles, while the proposed approach can assess the lighting quality of artistic portraits as human being does.
Xiaowu Chen; Xin Jin; Hongyu Wu; Qinping Zhao
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2014-11-12
Journal Detail:
Title:  IEEE transactions on image processing : a publication of the IEEE Signal Processing Society     Volume:  -     ISSN:  1941-0042     ISO Abbreviation:  IEEE Trans Image Process     Publication Date:  2014 Nov 
Date Detail:
Created Date:  2014-11-14     Completed Date:  -     Revised Date:  2014-11-15    
Medline Journal Info:
Nlm Unique ID:  9886191     Medline TA:  IEEE Trans Image Process     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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