IEEE Trans Pattern Anal Mach Intell - Exemplar-Based Colour Constancy and Multiple Illumination.

Tópicos

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Resumo

Exemplar-based learning has recently gained interest from researchers in a variety of computer science domains because of the prevalence of large amounts of accessible data and storage capacity. Applying the concept of exemplar-based learning to the problem of colour constancy seems odd at first glance since similar nearest neighbour images are not usually affected by precisely similar illuminants and gathering a dataset consisting of scenes for all possible illuminants is indeed impossible. In this paper we instead focus on \em{surfaces} in the image and address the colour constancy problem by unsupervised learning of an appropriate model for each surface in training images. We find models for each test-image surface and finally, illumination estimates are combined. We show state-of-the-art performance compared to current colour constancy algorithms, including when learning based on one image dataset is applied to tests from a different dataset. The proposed method has the advantage of overcoming multi-illuminant situations, which is not possible for most current methods. We test our technique on images with two distinct sources of illumination using a multiple-illuminant colour constancy dataset. The concept proposed here is a completely new approach to the colour constancy problem and provides a simple learning-based framework.

Resumo Limpo

exemplarbas learn recent gain interest research varieti comput scienc domain preval larg amount access data storag capac appli concept exemplarbas learn problem colour constanc seem odd first glanc sinc similar nearest neighbour imag usual affect precis similar illumin gather dataset consist scene possibl illumin inde imposs paper instead focus emsurfac imag address colour constanc problem unsupervis learn appropri model surfac train imag find model testimag surfac final illumin estim combin show stateoftheart perform compar current colour constanc algorithm includ learn base one imag dataset appli test differ dataset propos method advantag overcom multiillumin situat possibl current method test techniqu imag two distinct sourc illumin use multipleillumin colour constanc dataset concept propos complet new approach colour constanc problem provid simpl learningbas framework

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