Oil-Painting Style Classification Using ResNet with Conditional Information Bottleneck Regularization
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, buil...
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2025-06-01
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| author | Yaling Dang Fei Duan Jia Chen |
| author_facet | Yaling Dang Fei Duan Jia Chen |
| author_sort | Yaling Dang |
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| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>I</mi><mo stretchy="false">(</mo><mi>X</mi><mo>;</mo><mi>Z</mi><mo stretchy="false">)</mo></mrow></semantics></math></inline-formula> between input <i>X</i> and latent representation <i>Z</i>, our CIB minimizes the conditional mutual information <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>I</mi><mo stretchy="false">(</mo><mi>X</mi><mo>;</mo><mi>Z</mi><mo>∣</mo><mi>Y</mi><mo stretchy="false">)</mo></mrow></semantics></math></inline-formula>, where <i>Y</i> denotes the painting’s style label. We implement this conditional term using a matrix-based Rényi’s 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>13.1</mn><mo>%</mo></mrow></semantics></math></inline-formula> on Pandora and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>11.9</mn><mo>%</mo></mrow></semantics></math></inline-formula> on OilPainting. Beyond quantitative gains, our approach yields more disentangled latent representations that cluster semantically similar styles, facilitating interpretability. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2025-06-01 |
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| spelling | doaj-art-b7fae7f0fbce4704b93ec705385f33f42025-08-20T03:32:26ZengMDPI AGEntropy1099-43002025-06-0127767710.3390/e27070677Oil-Painting Style Classification Using ResNet with Conditional Information Bottleneck RegularizationYaling Dang0Fei Duan1Jia Chen2School of Art and Design, Shanxi University of Electronic Science and Technology, Linfen 041000, ChinaDepartment of Fine Arts and Craft Design, Yuncheng University, Yuncheng 044030, ChinaSchool of Art and Design, Shanxi University of Electronic Science and Technology, Linfen 041000, ChinaAutomatic 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>I</mi><mo stretchy="false">(</mo><mi>X</mi><mo>;</mo><mi>Z</mi><mo stretchy="false">)</mo></mrow></semantics></math></inline-formula> between input <i>X</i> and latent representation <i>Z</i>, our CIB minimizes the conditional mutual information <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>I</mi><mo stretchy="false">(</mo><mi>X</mi><mo>;</mo><mi>Z</mi><mo>∣</mo><mi>Y</mi><mo stretchy="false">)</mo></mrow></semantics></math></inline-formula>, where <i>Y</i> denotes the painting’s style label. We implement this conditional term using a matrix-based Rényi’s 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>13.1</mn><mo>%</mo></mrow></semantics></math></inline-formula> on Pandora and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>11.9</mn><mo>%</mo></mrow></semantics></math></inline-formula> on OilPainting. Beyond quantitative gains, our approach yields more disentangled latent representations that cluster semantically similar styles, facilitating interpretability.https://www.mdpi.com/1099-4300/27/7/677oil-painting style classificationconditional information bottleneckmatrix-based Rényi’s α-order entropy functional |
| spellingShingle | Yaling Dang Fei Duan Jia Chen Oil-Painting Style Classification Using ResNet with Conditional Information Bottleneck Regularization Entropy oil-painting style classification conditional information bottleneck matrix-based Rényi’s α-order entropy functional |
| title | Oil-Painting Style Classification Using ResNet with Conditional Information Bottleneck Regularization |
| title_full | Oil-Painting Style Classification Using ResNet with Conditional Information Bottleneck Regularization |
| title_fullStr | Oil-Painting Style Classification Using ResNet with Conditional Information Bottleneck Regularization |
| title_full_unstemmed | Oil-Painting Style Classification Using ResNet with Conditional Information Bottleneck Regularization |
| title_short | Oil-Painting Style Classification Using ResNet with Conditional Information Bottleneck Regularization |
| title_sort | oil painting style classification using resnet with conditional information bottleneck regularization |
| topic | oil-painting style classification conditional information bottleneck matrix-based Rényi’s α-order entropy functional |
| url | https://www.mdpi.com/1099-4300/27/7/677 |
| work_keys_str_mv | AT yalingdang oilpaintingstyleclassificationusingresnetwithconditionalinformationbottleneckregularization AT feiduan oilpaintingstyleclassificationusingresnetwithconditionalinformationbottleneckregularization AT jiachen oilpaintingstyleclassificationusingresnetwithconditionalinformationbottleneckregularization |