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In the paper a new proposal of semi-automatic method of craquelure detection in old paintings is presented. It is well known, that craquelure pattern is a unique feature and its character gives a significant information about the overall condition of the work, progress and cause of its degradation and helps in dating as well as confirming the authentication of the work. There exist methods, mostly deriving from other ridge and valley recognition problems, like geodesic or medical image feature segmentation based on watershed transform, morphological operations and region growing algorithm but they sometimes fail because of a complex nature of a craquelure pattern or large scale of an analyzed area. In this work a method is presented continuing a known semi-automatic technique based on a region growing algorithm. The novel approach is to apply a decision tree based pixel segmentation method to indicate the start points of craquelure pattern. The main difficulty in this mathod is defining an adequate set of descriptors forming a feature vector for the mining model.

Feature Vector Definition for a Decision Tree Based Craquelure Identification in Old Paintings