TY - JOUR
KW - Support Vector Machine
KW - Human-Computer Interaction (HCI)
KW - Artificial Neural Networks
KW - Sparse Metric Learning
KW - Feature Extraction
KW - Machine Learning
KW - Visual Arts Education
KW - Digitalization of Paintings
AU - Fei Xu
AU - Tong Wu
AU - Shali Huang
AU - Kuntong Han
AU - Wenwen Lin
AU - Shizhong Wu
AU - Sivaparthipan CB
AU - Samuel R Dinesh Jackson
AB - 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.
IS - Regular Issue
M1 - 2
N2 - 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.
PY - 2021
SP - 224
EP - 231
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Extensive Classification of Visual Art Paintings for Enhancing Education System using Hybrid SVM-ANN with Sparse Metric Learning based on Kernel Regression
UR - https://www.ijimai.org/journal/sites/default/files/2021-11/ijimai7_2_19_0.pdf
VL - 7
SN - 1989-1660
ER -