Authors: Janković, Radmila 
Affiliations: Mathematical Institute of the Serbian Academy of Sciences and Arts 
Title: Machine learning models for cultural heritage image classification: Comparison based on attribute selection
Journal: Information (Switzerland)
Volume: 11
Issue: 1
Issue Date: 1-Jan-2020
ISSN: 2078-2489
DOI: 10.3390/info11010012
Image classification is one of the most important tasks in the digital era. In terms of cultural heritage, it is important to develop classification methods that obtain good accuracy, but also are less computationally intensive, as image classification usually uses very large sets of data. This study aims to train and test four classification algorithms: (i) the multilayer perceptron, (ii) averaged one dependence estimators, (iii) forest by penalizing attributes, and (iv) the k-nearest neighbor rough sets and analogy based reasoning, and compares these with the results obtained from the Convolutional Neural Network (CNN). Three types of features were extracted from the images: (i) the edge histogram, (ii) the color layout, and (iii) the JPEG coefficients. The algorithms were tested before and after applying the attribute selection, and the results indicated that the best classification performance was obtained for the multilayer perceptron in both cases.
Keywords: Classification | Cultural heritage | Machine learning | WEKA
Publisher: MDPI
Project: Development of new information and communication technologies, based on advanced mathematical methods, with applications in medicine, telecommunications, power systems, protection of national heritage and education 

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