<abstract xmlns="http://www.w3.org/1999/xhtml"><p> In this paper a new approach to image segmentation was discussed. A model based on a data mining algorithm set on a pixel level of an image was introduced and implemented to solve the task of identification of craquelure and retouch traces in digital images of artworks. Both craquelure and retouch identification are important steps in art restoration process. Since the main goal is to classify and understand the cause of damage, as well as to forecast its further enlargement, a proper tool for a precise detection of the damaged area is needed. However, the complex nature of the pattern is a reason why a simple, universal detection algorithm is not always possible to be implemented. Algorithms presented in this work apply mining structures which depend of expandable set of attributes forming a feature vector, and thus offer an elastic structure for analysis. The result obtained by our method in craquelure segmentation was improved comparing to the results achieved by mathematical morphology methods, which was confirmed by a qualitative analysis. </p></abstract>

In this paper a new approach to image segmentation was discussed. A model based on a data mining algorithm set on a pixel level of an image was introduced and implemented to solve the task of identification of craquelure and retouch traces in digital images of artworks. Both craquelure and retouch identification are important steps in art restoration process. Since the main goal is to classify and understand the cause of damage, as well as to forecast its further enlargement, a proper tool for a precise detection of the damaged area is needed. However, the complex nature of the pattern is a reason why a simple, universal detection algorithm is not always possible to be implemented. Algorithms presented in this work apply mining structures which depend of expandable set of attributes forming a feature vector, and thus offer an elastic structure for analysis. The result obtained by our method in craquelure segmentation was improved comparing to the results achieved by mathematical morphology methods, which was confirmed by a qualitative analysis.

Data Mining Approach to Image Feature Extraction in Old Painting Restoration

University of Bielsko-Biala Department of Mechanics, Willowa 2, 43-309, Bielsko-Biala, Poland

National Museum in Krakow, Laboratory of Analysis and Non-Destructive Investigation of Her- itage Objects, Pilsudskiego 14, 31-106, Krakow, Poland

Foundations of Computing and Decision Sciences

{"article-title":"Data Mining Approach to Image Feature Extraction in Old Painting Restoration"}

In this paper a new approach to image segmentation was discussed. A model based on a data mining algorithm set on a pixel level of an image was introduced and...

Data Mining Approach to Image Feature Extraction in Old Painting Restoration

Publié en ligne: 27 sept. 2013

Pages: 159 - 174

DOI: https://doi.org/10.2478/fcds-2013-0007

Mots clés
data mining application, image processing, k-means clustering, decision tree based image segmentation, virtual restoration of paintings

This content is open access.

Data Mining Approach to Image Feature Extraction in Old Painting Restoration

Publié en ligne: 27 sept. 2013

Pages: 159 - 174

DOI: https://doi.org/10.2478/fcds-2013-0007

Mots clésdata mining application, image processing, k-means clustering, decision tree based image segmentation, virtual restoration of paintings

This content is open access.

Mots clés
data mining application, image processing, k-means clustering, decision tree based image segmentation, virtual restoration of paintings