Open AccessNatural image illuminant estimation via deep non‐negative matrix factorisation
Author(s) -
Liu Xiaopeng,
Zhong Guoqiang,
Dong Junyu
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2016.1058
Subject(s) - standard illuminant , artificial intelligence , color constancy , pooling , computer vision , metric (unit) , pattern recognition (psychology) , computer science , mathematics , matrix (chemical analysis) , image (mathematics) , operations management , materials science , composite material , economics
The influence of environmental light sources affects the colour cast in natural images. In computer vision, biased colours have a significant influence on object recognition and classification. Illuminant estimation aims to eliminate these effects and obtain the image in canonical white light. In this study, the authors propose a deep non‐negative matrix factorisation (DeepNMF) method to estimate the illuminant of colour‐biased images. DeepNMF deeply factorises the input matrix into multiple layers, separating the image into patches and reshaping each channel of the patch as an [ R , G , B ] matrix. Based on the diagonal model, they assume that the final layer is the estimated illuminant of each patch. Mean pooling is then used to estimate the illuminant of the overall image. The angular error is used as a metric to test the authors’ method on three commonly used colour constancy datasets. The results show that the proposed method is comparable to state‐of‐the‐art methods, although it is simpler to implement. As the proposed method uses a single image as input, it does not require a learning process.