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Image aesthetics assessment using composite features from transformer and CNN

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Abstract

As a popular research problem in computational aesthetics, image aesthetic assessment has many important applications in image editing, retrieval, and recommendation. However, the existing mainstream CNN-based image aesthetic assessment methods are difficult to obtain the global aesthetic attributes of images well. To this end, we propose a two-stream image aesthetic assessment model that couples Transformer and CNN features. We use the traditional CNN network to extract the image’s local aesthetic feature in the first stream, apply the superpixel algorithm to segment the image, and then feed the segmented image region into the Transformer network to learn the image’s aesthetic global features in the second stream. Finally, the features learned by Transformer and CNN are fused to achieve the image aesthetic assessment. The experimental results on the AVA dataset show that our proposed method can obtain local and global aesthetic information on images, which enables the model to learn richer aesthetic information, and the combination of whole and part is more in line with human aesthetic characteristics. Our proposed model achieves an accuracy of 84.5% in the classification task, achieving optimal performance compared to existing methods and good performance in the other two tasks (Score Regression and Distribution).

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References

  1. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84–90 (2017). https://doi.org/10.1145/3065386

    Article  Google Scholar 

  2. She, D., Lai, Y.-K., Yi, G., Xu, K.: Hierarchical layout-aware graph convolutional network for unified aesthetics assessment. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 8471–8480 (2021). https://doi.org/10.1109/CVPR46437.2021.00837

  3. Vaswani, A., Shazeer, N.M., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In Presented at the NIPS June 12 (2017)

  4. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv. (2020)

  5. Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: deformable transformers for end-to-end object detection. arXiv (2020)

  6. Strudel, R., Garcia, R., Laptev, I., Schmid, C.: Segmenter: transformer for semantic segmentation. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV). pp. 7242–7252 (2021). https://doi.org/10.1109/ICCV48922.2021.00717

  7. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34, 2274–2282 (2012). https://doi.org/10.1109/TPAMI.2012.120

    Article  Google Scholar 

  8. Lu, X., Lin, Z., Jin, H., Yang, J., Wang, J.Z.: RAPID: rating pictorial aesthetics using deep learning. In: Proceedings of the 22nd ACM International Conference on Multimedia. pp. 457–466 (2014). https://doi.org/10.1145/2647868.2654927

  9. Talebi, H., Milanfar, P.: NIMA: neural image assessment. IEEE Trans. Image Process. 27, 3998–4011 (2018). https://doi.org/10.1109/TIP.2018.2831899

    Article  MathSciNet  MATH  Google Scholar 

  10. Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv (2017)

  11. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR. (2014)

  12. Szegedy, C., Wei Liu, Yangqing Jia, Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1–9 (2015). https://doi.org/10.1109/CVPR.2015.7298594

  13. Wu, O., Hu, W., Gao, J: Learning to predict the perceived visual quality of photos. In: 2011 International Conference on Computer Vision. pp. 225–232 (2011). https://doi.org/10.1109/ICCV.2011.6126246

  14. Kong, S., Shen, X., Lin, Z., Mech, R., Fowlkes, C.: Photo aesthetics ranking network with attributes and content adaptation. Vol. 9905, pp. 662–679 (2016). https://doi.org/10.1007/978-3-319-46448-0_40

  15. Gao, F., Li, Z., Yu, J., Yu, J., Huang, Q., Tian, Q.: Style-adaptive photo aesthetic rating via convolutional neural networks and multi-task learning. Neurocomputing 395, 247–254 (2020). https://doi.org/10.1016/j.neucom.2018.06.099

    Article  Google Scholar 

  16. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90

  17. Murray, N., Marchesotti, L., Perronnin, F.: AVA: a large-scale database for aesthetic visual analysis. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition. pp. 2408–2415 (2012). https://doi.org/10.1109/CVPR.2012.6247954

  18. Yang, Y., Xu, L., Li, L., Qie, N., Li, Y., Zhang, P., Guo, Y.: Personalized image aesthetics assessment with rich attributes. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 19829–19837 (2022). https://doi.org/10.1109/CVPR52688.2022.01924

  19. Zhu, H., Zhou, Y., Li, L., Li, Y., Guo, Y.: Learning personalized image aesthetics from subjective and objective attributes. IEEE Trans. Multimedia 25, 179–190 (2023). https://doi.org/10.1109/TMM.2021.3123468

    Article  Google Scholar 

  20. Zhu, H., Li, L., Wu, J., Zhao, S., Ding, G., Shi, G.: Personalized image aesthetics assessment via meta-learning with bilevel gradient optimization. IEEE Trans. Cybern. 52, 1798–1811 (2022). https://doi.org/10.1109/TCYB.2020.2984670

    Article  Google Scholar 

  21. Liu, D., Puri, R., Kamath, N., Bhattacharya, S.: Composition-aware image aesthetics assessment. In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 3558–3567 (2020). https://doi.org/10.1109/WACV45572.2020.9093412

  22. Peng, Z., Huang, W., Gu, S., Xie, L., Wang, Y., Jiao, J., Ye, Q.: Conformer: local features coupling global representations for visual recognition. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV). pp. 357–366 (2021). https://doi.org/10.1109/ICCV48922.2021.00042

  23. Srinivas, A., Lin, T.-Y., Parmar, N., Shlens, J., Abbeel, P., Vaswani, A.: bottleneck transformers for visual recognition. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 16514–16524 (2021). https://doi.org/10.1109/CVPR46437.2021.01625

  24. Guo, J., Han, K., Wu, H., Tang, Y., Chen, X., Wang, Y., Xu, C.: CMT: Convolutional neural networks meet vision transformers. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 12165–12175 (2022). https://doi.org/10.1109/CVPR52688.2022.01186

  25. Wu, H., Xiao, B., Codella, N., Liu, M., Dai, X., Yuan, L., Zhang, L.: CvT: introducing convolutions to vision transformers. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV). pp. 22–31 (2021). https://doi.org/10.1109/ICCV48922.2021.00009

  26. Li, K., Wang, Y., Gao, P., Song, G., Liu, Y., Li, H., Qiao, Y.: UniFormer: unified transformer for efficient spatiotemporal representation learning. arXiv (2022)

  27. Deng, J., Dong, W., Socher, R., Li, L.-J., Kai, L., Li, F.-F.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. pp. 248–255 (2009). https://doi.org/10.1109/CVPR.2009.5206848

  28. Achanta, R., Susstrunk, S.: Superpixels and polygons using simple non-iterative clustering. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4895–4904 (2017). https://doi.org/10.1109/CVPR.2017.520

  29. Van Den Bergh, M., Boix, X., Roig, G., Van Gool, L.: SEEDS: superpixels extracted via energy-driven sampling. Int. J. Comput. Vis. 111, 298–314 (2015). https://doi.org/10.1007/s11263-014-0744-2

    Article  MathSciNet  Google Scholar 

  30. Yao, J., Boben, M., Fidler, S., Urtasun, R.: Real-time coarse-to-fine topologically preserving segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2947–2955 (2015). https://doi.org/10.1109/CVPR.2015.7298913

  31. Li, L., Zhu, H., Zhao, S., Ding, G., Lin, W.: Personality-assisted multi-task learning for generic and personalized image aesthetics assessment. IEEE Trans. Image Process. 29, 3898–3910 (2020). https://doi.org/10.1109/TIP.2020.2968285

    Article  MATH  Google Scholar 

  32. Chen, Q., Zhang, W., Zhou, N., Lei, P., Xu, Y., Zheng, Y., Fan, J.: Adaptive fractional dilated convolution network for image aesthetics assessment. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 14102–14111 (2020). https://doi.org/10.1109/CVPR42600.2020.01412

  33. Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2921–2929 (2016). https://doi.org/10.1109/CVPR.2016.319

  34. Ma, S., Liu, J., Chen, C.W.: A-lamp: adaptive layout-aware multi-patch deep convolutional neural network for photo aesthetic assessment. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 722–731 (2017). https://doi.org/10.1109/CVPR.2017.84

  35. Fu, X., Yan, J., Fan, C.: Image aesthetics assessment using composite features from off-the-shelf deep models. In: 2018 25th IEEE International Conference on Image Processing (ICIP). pp. 3528–3532 (2018). https://doi.org/10.1109/ICIP.2018.8451133

  36. Hosu, V., Goldlucke, B., Saupe, D.: Effective aesthetics prediction with multi-level spatially pooled features. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9367–9375 (2019). https://doi.org/10.1109/CVPR.2019.00960

  37. Ko, K., Lee, J.-T., Kim, C.-S.: PAC-Net: pairwise aesthetic comparison network for image aesthetic assessment. In: 2018 25th IEEE International Conference on Image Processing (ICIP). pp. 2491–2495 (2018). https://doi.org/10.1109/ICIP.2018.8451621

  38. Lee, J.-T., Kim, C.-S.: Image aesthetic assessment based on pairwise comparison—a unified approach to score regression, binary classification, and personalization. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV). pp. 1191–1200 (2019). https://doi.org/10.1109/ICCV.2019.00128

  39. Zeng, H., Cao, Z., Zhang, L., Bovik, A.C.: A unified probabilistic formulation of image aesthetic assessment. IEEE Trans. Image Process. 29, 1548–1561 (2020). https://doi.org/10.1109/TIP.2019.2941778

    Article  MathSciNet  MATH  Google Scholar 

  40. Murray, N., Gordo, A.: A deep architecture for unified aesthetic prediction. arXiv (2017)

  41. Sheng, K., Dong, W., Ma, C., Mei, X., Huang, F., Hu, B.-G.: Attention-based multi-patch aggregation for image aesthetic assessment. In: Proceedings of the 26th ACM international conference on Multimedia. pp. 879–886 (2018). https://doi.org/10.1145/3240508.3240554

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Authors and Affiliations

  1. School of Computer Science and Technology, Tiangong University, No. 399 Binshui Road, Tianjin, 300387, China

    Yongzhen Ke, Yin Wang, Kai Wang, Jing Guo & Shuai Yang

  2. Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tianjin, 300387, China

    Yongzhen Ke, Yin Wang, Kai Wang, Jing Guo & Shuai Yang

  3. Business School, Nankai University, Tianjin, 300071, China

    Fan Qin

  4. National Demonstration Center for Experimental Engineering Training Education (Tiangong University), Tianjin, 300387, China

    Yongzhen Ke

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  1. Yongzhen Ke
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Contributions

Yongzhen Ke: Conceptualization, Methodology, Supervision, Project administration. Yin Wang: Methodology, Software, Writing - Original Draft, Writing - Review & Editing Kai Wang: Methodology, Software, Writing - Original Draft. Fan Qin: Validation, Writing - Review & Editing Jing Guo: Writing - Review & Editing, Formal analysis, Visualization. Shuai Yang: Resources, Validation, Data Curation.

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Correspondence to Kai Wang.

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Communicated by B. Bao.

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Ke, Y., Wang, Y., Wang, K. et al. Image aesthetics assessment using composite features from transformer and CNN. Multimedia Systems 29, 2483–2494 (2023). https://doi.org/10.1007/s00530-023-01141-7

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