02385nas a2200337   4500000000100000000000100001008004100002260001200043653002700055653003700082653003100119653002700150653002300177653002100200653002600221653003200247100001100279700001200290700001600302700001600318700001500334700001600349700002100365700002800386245016000414856008200574300001200656490000600668520135900674022001402033       2021                            d  c12/202110aSupport Vector Machine10aHuman-Computer Interaction (HCI)10aArtificial Neural Networks10aSparse Metric Learning10aFeature Extraction10aMachine Learning10aVisual Arts Education10aDigitalization of Paintings1 aFei Xu1 aTong Wu1 aShali Huang1 aKuntong Han1 aWenwen Lin1 aShizhong Wu1 aSivaparthipan CB1 aSamuel R Dinesh Jackson00aExtensive Classification of Visual Art Paintings for Enhancing Education System using Hybrid SVM-ANN with Sparse Metric Learning based on Kernel Regression  uhttps://www.ijimai.org/journal/sites/default/files/2021-11/ijimai7_2_19_0.pdf  a224-2310 v73 aIn 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.  a1989-1660