In this paper, we explore the potential of sparse representation classification (SRC) in digital painting analysis, with the aim to aid the conservation/restoration treatment of old masterpieces. The focus is on detecting paint losses using multimodal acquisitions (such as optical images taken at different time instances before and during the conservation treatment, infrared images and digital radiography images). While SRC has been applied before in different scenarios, the present application requires some specific adaptations due to the nature and the size of the data, as well as the uncertainty to the labelled samples. Our initial results are very promising, compared to some more traditional or commonly used classification approaches, such as linear regression classification and support vector machines.
|Number of pages||3|
|Publication status||Published - 2016|
|Event||iTWIST 2016: international - Traveling Workshop on Interactions between Sparse models and Technology - Aalborg, Denmark|
Duration: 24 Aug 2016 → 26 Aug 2016
|Workshop||iTWIST 2016: international - Traveling Workshop on Interactions between Sparse models and Technology|
|Period||24/08/16 → 26/08/16|
- paint loss detection
- sparse representation classification (SRC)
- image classification
Huang, S., Liao, W., Zhang, H., & Pizurica, A. (2016). Paint loss detection in old paintings by sparse representation classification. 62-64. Paper presented at iTWIST 2016: international - Traveling Workshop on Interactions between Sparse models and Technology, Aalborg, Denmark.