{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:26:34Z","timestamp":1760059594216,"version":"build-2065373602"},"reference-count":57,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T00:00:00Z","timestamp":1750809600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"2024 Shanxi Provincial Art and Science Planning Project","award":["24BF037"],"award-info":[{"award-number":["24BF037"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Automatic classification of oil-painting styles holds significant promise for art history, digital archiving, and forensic investigation by offering objective, scalable analysis of visual artistic attributes. In this paper, we introduce a deep conditional information bottleneck (CIB) framework, built atop ResNet-50, for fine-grained style classification of oil paintings. Unlike traditional information bottleneck (IB) approaches that minimize the mutual information I(X;Z) between input X and latent representation Z, our CIB minimizes the conditional mutual information I(X;Z\u2223Y), where Y denotes the painting\u2019s style label. We implement this conditional term using a matrix-based R\u00e9nyi\u2019s entropy estimator, thereby avoiding costly variational approximations and ensuring computational efficiency. We evaluate our method on two public benchmarks: the Pandora dataset (7740 images across 12 artistic movements) and the OilPainting dataset (19,787 images across 17 styles). Our method outperforms the prevalent ResNet with a relative performance gain of 13.1% on Pandora and 11.9% on OilPainting. Beyond quantitative gains, our approach yields more disentangled latent representations that cluster semantically similar styles, facilitating interpretability.<\/jats:p>","DOI":"10.3390\/e27070677","type":"journal-article","created":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T06:55:13Z","timestamp":1750920913000},"page":"677","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Oil-Painting Style Classification Using ResNet with Conditional Information Bottleneck Regularization"],"prefix":"10.3390","volume":"27","author":[{"given":"Yaling","family":"Dang","sequence":"first","affiliation":[{"name":"School of Art and Design, Shanxi University of Electronic Science and Technology, Linfen 041000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Duan","sequence":"additional","affiliation":[{"name":"Department of Fine Arts and Craft Design, Yuncheng University, Yuncheng 044030, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Art and Design, Shanxi University of Electronic Science and Technology, Linfen 041000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Karayev, S., Hertzmann, A., Trentacoste, M., Han, H., Winnemoeller, H., Agarwala, A., and Darrell, T. 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