IEEE Trans Image Process - Learning templates for artistic portrait lighting analysis.

Tópicos

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Resumo

Lighting is a key factor in creating impressive artistic portraits. In this paper, we propose to analyze portrait lighting by learning templates of lighting styles. Inspired by the experience of artists, we first define several novel features that 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 that 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 is defined to measure the difference between two portrait lightings. Experimental results show that the learned templates can well describe the lighting styles, whereas the proposed approach can assess the lighting quality of artistic portraits as human being does.

Resumo Limpo

light key factor creat impress artist portrait paper propos analyz portrait light learn templat light style inspir experi artist first defin sever novel featur describ local contrast various face region inform featur select stepwis featur pursuit algorithm deriv templat various light style match score measur similar test portrait templat calcul light style classif furthermor train regress model subject score featur respons templat predict score portrait light qualiti base templat novel face illumin descriptor defin measur differ two portrait light experiment result show learn templat can well describ light style wherea propos approach can assess light qualiti artist portrait human

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