MACHINE LEARNING ALGORITHMS FOR CLASSIFYING VISUAL ARTISTIC STYLES ACROSS HISTORICAL PERIODS
DOI:
https://doi.org/10.29121/shodhkosh.v7.i4s.2026.7464Keywords:
Artistic Style Classification, Machine Learning, Deep Learning, Convolutional Neural Networks, Image Feature Extraction, Visual Art AnalysisAbstract [English]
A broad spectrum of interdisciplinary research problems have been identified in the homogeneous grouping of visual artistic styles through time: as a research topic at the precincts of machine learning and art history. This paper introduces a unified methodology of automatic detection of artistic styles with traditional machine learning algorithms as well as with current deep learning architectures. First, local handcrafted feature extraction methods such as Scale-Invariant Feature Transform (SIFT), Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) are used to extract low-level visual features of the image such as texture, edges, and structure composition. These characteristics are also supplemented by color histograms and spatial composition descriptors to improve the ability to represent. Multi-class style categorization is then done using classical classifiers that include Support Vector Machines (SVM), K-Nearest Neighbors (KNN) and Random Forest. On the same note, Convolutional Neural Networks (CNN), ResNet, VGG, and EfficientNet are deep learning models that are trained to extract high-level abstract features directly using image data. The paper also examines the hybrid methods which can combine the handcrafted and deep features to enhance the accuracy of the classification. Empirical evidence shows that deep learning models are more effective and accurate in classification with a classification accuracy of over 90 percent in standard art data. The proposed framework is effective and scalable with respect to automated artistic style recognition and it helps in preservation, curation, and analysis of the digital art.
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Copyright (c) 2026 Dr. Pratibha V. Kashid, Ganesh Korwar, Kiran Shyam Khandare, Baoxin Le, Pramod Rahate, Nikita P. Katariya, Suresh Arumugam
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