Research Article
Benchmarking a New Dataset of Traditional Balinese Carving Ornaments for Image Classification Task
@INPROCEEDINGS{10.4108/eai.27-11-2021.2315534, author={Made Windu Antara Kesiman and I Gede Mahendra Darmawiguna and I Gede Rusdy Mahayana Putra and Ni Luh Putu Kurniawati}, title={Benchmarking a New Dataset of Traditional Balinese Carving Ornaments for Image Classification Task}, proceedings={Proceedings of the 4th International Conference on Vocational Education and Technology, IConVET 2021, 27 November 2021, Singaraja, Bali, Indonesia}, publisher={EAI}, proceedings_a={ICONVET}, year={2022}, month={2}, keywords={benchmark dataset image classification balinese carving ornament}, doi={10.4108/eai.27-11-2021.2315534} }- Made Windu Antara Kesiman
I Gede Mahendra Darmawiguna
I Gede Rusdy Mahayana Putra
Ni Luh Putu Kurniawati
Year: 2022
Benchmarking a New Dataset of Traditional Balinese Carving Ornaments for Image Classification Task
ICONVET
EAI
DOI: 10.4108/eai.27-11-2021.2315534
Abstract
In the framework of the development of an automatic Balinese carving ornament recognition application, a valid image dataset is needed. This paper describes the improved new dataset of traditional Balinese carving ornaments and presents the benchmarking results in an image classification task. The improvement of the new dataset involves the increased number of image samples and involves the validation and addition of the number of ornament classes. Some frequently used feature extraction methods, for example, Gabor Filter, Zoning, Histogram of Gradient, Neighborhood Pixels Weights, and Kirsch edge, were tested to benchmark the image classification task for this new dataset. The benchmark results showed that this new dataset has a fairly high technical challenge for feature extraction methods in the pattern recognition field. The new proposed dataset will support further research steps in building a classification and recognition system for Balinese carving ornaments.
