Statistical feature based craquelure classification

Authors

Irene Crisologo ⋅ PH National Institute of Physics, University of the Philippines Diliman
Maricor Soriano ⋅ PH National Institute of Physics, University of the Philippines Diliman
Christopher Monterola ⋅ PH National Institute of Physics, University of the Philippines Diliman

Abstract

We demonstrate an automatic procedure for extracting features such as directionality of crack patterns, distribution of island sizes, and crack segment lengths to aid in automatic classification of painting cracks or craquelures. To test our classifier, we make use of four distinct craquelure patterns, designated by names based on its country of origin, namely: Dutch, Flemish, French or Italian. We report that using the above statistical measures, linear discriminant analysis can classify the craquelure patterns with an 85% accuracy. This accuracy is three times better than mere chance classification, or the proportional chance criterion, computed to be 26%. The article hopes to serve as basis in which the appropriateness of models for craquelure formation can be tested.

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Issue

2009: Proceedings of the 27th Samahang Pisika ng Pilipinas Physics Congress

Article ID

SPP-2009-5A-03

Section

Instrumentation and Art

Published

2009-10-28

How to Cite

[1]

I Crisologo, M Soriano, and C Monterola, Statistical feature based craquelure classification, Proceedings of the Samahang Pisika ng Pilipinas 27, SPP-2009-5A-03 (2009). URL: https://proceedings.spp-online.org/article/view/SPP-2009-5A-03.