@article{3022,
  keywords = {Support Vector Machine, Human-Computer Interaction (HCI), Artificial Neural Networks, Sparse Metric Learning, Feature Extraction, Machine Learning, Visual Arts Education, Digitalization of Paintings},
  author = {Fei Xu and Tong Wu and Shali Huang and Kuntong Han and Wenwen Lin and Shizhong Wu and Sivaparthipan CB and Samuel R Dinesh Jackson},
  title = {Extensive Classification of Visual Art Paintings for Enhancing Education System using Hybrid SVM-ANN with Sparse Metric Learning based on Kernel Regression},
  abstract = {In recent decades, the collection of visual art paintings is large, digitized, and available for public uses that are rapidly growing. The development of multi-media systems is needed due to the huge amount of digitized artwork collections for retrieving and archiving this large-scale data. This multimedia system benefits from high-level tasks and has an essential step for measuring the similarity of visual between the artistic items. For modeling the similarities between the artworks or paintings, it is essential to extract useful features of visual paintings and propose the best approach for learning these similarity metrics. The infield of visual arts education, knowing the similarities and features, makes education more attractive by enhancing cognitive development in students. In this paper, the detailed visual features are listed, and the similarity measurement between the paintings is optimized by the Sparse Metric Learning-based Kernel Regression (KR-SML). A classification model is developed using hybrid SVM-ANN for semantic-level understanding to predict painting’s genre, artist, and style. Furthermore, the Human-Computer Interaction (HCI) based formulation model is built to analyze the proposed technique. The simulation results show that the proposed model is better in terms of performance than other existing techniques.},
  year = {2021},
  journal = {International Journal of Interactive Multimedia and Artificial Intelligence},
  volume = {7},
  number = {2},
  pages = {224-231},
  month = {12/2021},
  issn = {1989-1660},
  url = {https://www.ijimai.org/journal/sites/default/files/2021-11/ijimai7_2_19_0.pdf},
  doi = {10.9781/ijimai.2021.10.001},
}