Abstract
© 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). Digital documentation of cultural heritage images has emerged as an important topic in data analysis. Increasing the size and number of images to be processed making the task of categorizing them a challenging task and may take an inordinate amount of time. This research paper proposes a solution to the mentioned challenges by classifying the subject of the image of the study using Convolutional Neural Network. Classification of available images leads to improve the management of the images dataset and enhance the search of a specific item, which helps in the tasks of studying and analysis the proper heritage object. Deep learning for architectural heritage images classification has been employed during the course of this study. The pre-trained convolutional neural networks GoogLeNet, resnet18 and resnet50 proposed to be applied on public dataset Cultural Heritage images. Experimental results have shown promising outcomes with an accuracy of “87.91”, “95.47” and “95.57” respectively.
Document Type
Conference Proceeding
Date of Publication
2020
ISSN
16130073
Volume
2602
Publication Title
Proceedings of 2nd International Workshop on Visual Pattern Extraction and Recognition for Cultural Heritage Understandingco-located with 16th Italian Research Conference on Digital Libraries (IRCDL 2020)
Publisher
CEUR-WS Team
School
School of Engineering
RAS ID
35319
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
First Page
1
Last Page
12
Included in
Arts and Humanities Commons, Physical Sciences and Mathematics Commons, Social and Behavioral Sciences Commons
Comments
Abed, M. H., Al-Asfoor, M., & Hussain, Z. M. (January, 2020). Architectural heritage images classification using deep learning with CNN [Paper presentation]. Proceedings of the 2nd International Workshop on Visual Pattern Extraction and Recognition for Cultural Heritage Understanding, Bari, Italy. http://ceur-ws.org/Vol-2602/