19 December 2016 Architectural style classification of Mexican historical buildings using deep convolutional neural networks and sparse features
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Abstract
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.
© 2016 SPIE and IS&T
Abraham Montoya Obeso, Abraham Montoya Obeso, Jenny Benois-Pineau, Jenny Benois-Pineau, Alejandro Álvaro Ramirez Acosta, Alejandro Álvaro Ramirez Acosta, Mireya Saraí García Vázquez, Mireya Saraí García Vázquez, } "Architectural style classification of Mexican historical buildings using deep convolutional neural networks and sparse features," Journal of Electronic Imaging 26(1), 011016 (19 December 2016). https://doi.org/10.1117/1.JEI.26.1.011016 . Submission: Received: 2 July 2016; Accepted: 18 November 2016
Received: 2 July 2016; Accepted: 18 November 2016; Published: 19 December 2016
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