TY - JOUR
T1 - Architectural style classification of Mexican historical buildings using deep convolutional neural networks and sparse features
AU - Obeso, Abraham Montoya
AU - Benois-Pineau, Jenny
AU - Acosta, Alejandro Álvaro Ramirez
AU - Vázquez, Mireya Saraí García
N1 - Publisher Copyright:
© 2016 SPIE and IS&T.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - We propose a convolutional neural network to classify images of buildings using sparse features at the network's input in conjunction with primary color pixel values. As a result, a trained neuronal model is obtained to classify Mexican buildings in three classes according to the architectural styles: prehispanic, colonial, and modern with an accuracy of 88.01%. The problem of poor information in a training dataset is faced due to the unequal availability of cultural material. We propose a data augmentation and oversampling method to solve this problem. The results are encouraging and allow for prefiltering of the content in the search tasks.
AB - We propose a convolutional neural network to classify images of buildings using sparse features at the network's input in conjunction with primary color pixel values. As a result, a trained neuronal model is obtained to classify Mexican buildings in three classes according to the architectural styles: prehispanic, colonial, and modern with an accuracy of 88.01%. The problem of poor information in a training dataset is faced due to the unequal availability of cultural material. We propose a data augmentation and oversampling method to solve this problem. The results are encouraging and allow for prefiltering of the content in the search tasks.
KW - classification
KW - convolutional neural network
KW - cultural heritage
KW - deep learning
KW - image processing
KW - indexing
UR - http://www.scopus.com/inward/record.url?scp=85007240156&partnerID=8YFLogxK
U2 - 10.1117/1.JEI.26.1.011016
DO - 10.1117/1.JEI.26.1.011016
M3 - Artículo
SN - 1017-9909
VL - 26
JO - Journal of Electronic Imaging
JF - Journal of Electronic Imaging
IS - 1
M1 - 011016
ER -