RT info:eu-repo/semantics/article
T1 Classification of architectural heritage images using deep learning techniques
A1 Llamas, José
A1 Martín Lerones, Pedro
A1 Medina Aparicio, Roberto
A1 Zalama Casanova, Eduardo
A1 Gómez García-Bermejo, Jaime
K1 Image classification
K1 Deep learning
K1 Convolutional neural network
K1 Digital documentation
K1 Architectural heritage
K1 foto
AB The classification of the images taken during the measurement of an architectural asset is an essential task within the digital documentation of cultural heritage. A large number of images are usually handled, so their classification is a tedious task (and therefore prone to errors) and habitually consumes a lot of time. The availability of automatic techniques to facilitate these sorting tasks would improve an important part of the digital documentation process. In addition, a correct classification of the available images allows better management and more efficient searches through specific terms, thus helping in the tasks of studying and interpreting the heritage asset in question. The main objective of this article is the application of techniques based on deep learning for the classification of images of architectural heritage, specifically through the use of convolutional neural networks. For this, the utility of training these networks from scratch or only fine tuning pre-trained networks is evaluated. All this has been applied to classifying elements of interest in images of buildings with architectural heritage value. As no datasets of this type, suitable for network training, have been located, a new dataset has been created and made available to the public. Promising results have been obtained in terms of accuracy and it is considered that the application of these techniques can contribute significantly to the digital documentation of architectural heritage.
PB MDPI
YR 2017
FD 2017
LK https://uvadoc.uva.es/handle/10324/57233
UL https://uvadoc.uva.es/handle/10324/57233
LA eng
NO Applied Sciences, 2017, vol. 7, n. 10, p. 992
NO Producción Científica
DS UVaDOC
RD 21-nov-2024