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Article Dans Une Revue Journal of Electronic Imaging Année : 2017

Architectural style classification of Mexican historical buildings using deep convolutional neural networks and sparse features

Résumé

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.
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Dates et versions

hal-01436606 , version 1 (16-01-2017)

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Abraham Montoya Obeso, Jenny Benois-Pineau, Alejandro Álvaro Ramirez Acosta, Mireya Saraí García Vázquez. Architectural style classification of Mexican historical buildings using deep convolutional neural networks and sparse features. Journal of Electronic Imaging, 2017, Journal of Electronic Imaging - Special Section on Image Processing for Cultural Heritage 26 (1), pp.011016-011016. ⟨10.1117/1.JEI.26.1.011016⟩. ⟨hal-01436606⟩

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