A Content-based Analysis of Craquelure Patterns in Paintings

The advent of multimedia technology has offer new dimension to computerized applications. Art-based applications are among those which have and will continue to benefit from this advancement. With the ever growing size and variety of accessible data across museum collections, the need for flexible and efficient data retrieval is growing at an alarming rate. Content-based image retrieval (CBIR) and analysis is getting a lot of attention from museums and art institutions. One of the image-based requirements from museums, is to automatically classify craquelure (cracks) in paintings for the purpose of aiding damage assessment. Craquelure in paintings can be an important element in judging authenticity, use of material as well as environmental and physical impact because these can lead to different craquelure patterns. Mass screening of craquelure patterns will help to establish a better platform for conservators to identify cause of damage. As a way of performing such action, a content-based approach is seen as an appropriate path. This dissertation uncovers the main issues behind content-based craquelure analysis. The important steps namely, crack enhancement and detection are explained. We also implemented a chain-code based craquelure structuring to allow efficient feature extraction. A hierarchical craquelure feature representation is also developed in order to view features at multiple structural scale. Experiment results are presented to show the discriminating power of the features. Early strategies towards classifying the crack patterns are also described.

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