Abstract
Painter classification is a well-researched problem, which has been pursued mainly during the past two decades via traditional approaches of image processing and machine learning. In this paper, we propose a novel dual-stream architecture designed for capturing in parallel both global elements (e.g., scenery) and local structures (e.g., brushstrokes) in images of highly-varying resolutions and aspect ratios. Our proposed method yields 93.39% accuracy, which comprises an improvement of 1.66% (and an error rate reduction of 20%), compared to the previous state-of-the-art (SOTA) method on the same extensive dataset.
Nathan Netanyahu is also affiliated with the Department of Computer Science at the College of Law and Business, Ramat-Gan 5257346, Israel.
D. Nevo—The support of the Israeli Innovation Authority and Defender Cyber Technologies LTD under File No. 69098 of the NOFAR Academic Knowledge Guidance Program is gratefully acknowledged.
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Nevo, D., David, E.O., Netanyahu, N.S. (2022). DeepArtist: A Dual-Stream Network for Painter Classification of Highly-Varying Image Resolutions. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13532. Springer, Cham. https://doi.org/10.1007/978-3-031-15937-4_49
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