Special Section on Image Processing for Cultural Heritage

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

[+] Author Affiliations
Abraham Montoya Obeso

Instituto Politécnico Nacional-Centro de Investigación y Desarrollo de Tecnología Digital, Ave. Instituto Politécnico Nacional 1310, Tijuana 22435, México

Jenny Benois-Pineau

LaBRI UMR 5800, University of Bordeaux, 351 Cours de la Liberation, F-33405 Talence Cedex, France

Alejandro Álvaro Ramirez Acosta

MIRAL R&D&I, Imperial Beach, San Diego, California 91932, United States

Mireya Saraí García Vázquez

Instituto Politécnico Nacional-Centro de Investigación y Desarrollo de Tecnología Digital, Ave. Instituto Politécnico Nacional 1310, Tijuana 22435, México

MIRAL R&D&I, Imperial Beach, San Diego, California 91932, United States

J. Electron. Imaging. 26(1), 011016 (Dec 19, 2016). doi:10.1117/1.JEI.26.1.011016
History: Received July 2, 2016; Accepted November 18, 2016
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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.

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Citation

Abraham Montoya Obeso ; Jenny Benois-Pineau ; Alejandro Álvaro Ramirez Acosta and Mireya Saraí García Vázquez
"Architectural style classification of Mexican historical buildings using deep convolutional neural networks and sparse features", J. Electron. Imaging. 26(1), 011016 (Dec 19, 2016). ; http://dx.doi.org/10.1117/1.JEI.26.1.011016


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