Authors: Janković, Radmila 
Affiliations: Mathematical Institute of the Serbian Academy of Sciences and Arts 
Title: Algorithm Comparison for Cultural Heritage Image Classification
Journal: CEUR Workshop Proceedings
Volume: 2602
First page: 26
Last page: 33
Conference: 2nd International Workshop on Visual Pattern Extraction and Recognition for Cultural Heritage Understanding, VIPERC 2020; Bari; Italy; 29 January 2020
Issue Date: 2020
Rank: M33
Digitization represents an important part of the development of online systems. As such it includes, among other, the deployment, categorization and preservation of audio, video and textual contents online. Such process is especially interesting from the perspective of cultural heritage, as it allows the long-term preservation and sharing of culture worldwide. This study observes four classification algorithms: (i) themultilayer perceptron, (ii) averaged one dependence estimators, (iii) forest by penalizing attributes, and (iv) the k-nearest neighbor rough sets and analogy based reasoning, before and after attribute classifcation, and compares these with the results obtained from the convolutional neural network. The obtained results show that the best classification performance was achieved by the multilayer perceptron, followed by the convolutional neural network.
Publisher: CEUR

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