Bayesian crack detection in high resolution data

We propose a semi-supervised crack detection method that can be used for high-dimensional and multimodal acquisitions of paintings. Our dataset consists of a recent collection of images of the Ghent Altarpiece (1432), one of Northern Europe's most important art masterpieces. We build a classifier that is able to discern crack pixels from the background consisting of non-crack pixels, making optimal use of the information that is provided by each modality. To accomplish this we employ a recently developed non-parametric Bayesian classifier, that uses tensor factorizations to characterize any conditional probability. A prior is placed on the parameters of the factorization such that every possible interaction between predictors is allowed while still identifying a sparse subset among these predictors.