Special perceptual parsing for Chinese landscape painting scene understanding: a semantic segmentation approach

Rui Yang1,2, Honghong Yang3,2, Min Zhao1,2, Ru Jia1,2, Xiaojun Wu1,3,2, Yumei Zhang1,3,2
1School of Computer Science, Shaanxi Normal University, Chang’an, Xi’an, China
2Key Laboratory of Intelligent Computing and Service Technology for Folk Song, Ministry of Culture and Tourism, Xi’an, China
3Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an, China

Tóm tắt

The automatic and precise perceptual parsing of Chinese landscape paintings (CLP) significantly aids in the digitization and recreation of artworks. Manual extraction and analysis of objects in CLPs is challenging, even for expert painters with professional knowledge and sharp discernment. Two main key reasons restricted the development of CLP parsing: (1) a lack of pixel-level labeled data used to supervise model training, and (2) the inherent complexity of CLP images compared to real scenes, characterized by varied scales, diverse textures, and intricate empty spaces. To address these challenges, we first construct a pixel-level annotated CLP segmentation datasets to advance perceptual parsing. Then, a novel CLP Perceptual Parsing (CLPPP) model is designed to fully utilize the intrinsic features of CLP images. To dynamically and adaptively capture context information, we introduced a set of learnable kernels into the CLPPP model based on the multiscale features of objects within CLPs. This enabled the model to learn an appropriate receptive field for context information extraction. Additionally, a positional attention head is devised to effectively eliminate noise from the intergroup and help the kernel gain inter-object position information. This iterative optimization process is helpful to learn powerful feature representations for different textures in CLPs. The experiment results demonstrate that the proposed CLPPP model outperforms state-of-the-art methods with mIoU, aAcc, and mAcc scores of 55.45, 75.08, and 71.15, respectively, achieving a large margin on the CLP dataset under consistent conditions.

Tài liệu tham khảo

Bousselham W, Thibault G, Pagano L, Machireddy A, Gray J, Chang YH, Song X (2021) Efficient self-ensemble framework for semantic segmentation. arXiv preprint arXiv:2111.13280 Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. European conference on computer vision. Springer, Berlin, pp 213–229 Chatzistamatis S, Rigos A, Tsekouras GE (2020) Image recoloring of art paintings for the color blind guided by semantic segmentation. International conference on engineering applications of neural networks. Springer, Berlin, pp 261–273 Chen LC, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 Chen LC, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV) Cheng B, Misra I, Schwing AG, Kirillov A, Girdhar R (2022) Masked-attention mask transformer for universal image segmentation Cheng B, Misra I, Schwing AG, Kirillov A, Girdhar R (2022) Masked-attention mask transformer for universal image segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 1290–1299 Choi S, Kim JT, Choo J (2020) Cars can’t fly up in the sky: improving urban-scene segmentation via height-driven attention networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 9373–9383 Cohen N, Newman Y, Shamir A (022) Semantic segmentation in art paintings. In: Computer graphics forum, vol 41, pp 261–275. Wiley Online Library Cordts M, Omran M, Ramos S, Rehfeld T, Enzweiler M, Benenson R, Franke U, Roth S, chiele B (2016) The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3213–3223 Deng J, Dong W, Socher R, Li LJ, Li FF (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE computer society conference on computer vision and pattern recognition (CVPR 2009), 20-25 June 2009, Miami, Florida, USA Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 Everingham M, Eslami SA, Van Gool L, Williams CK, Winn J, Zisserman A (2015) The pascal visual object classes challenge: a retrospective. Int J Comput Vis 111(1):98–136 He J, Deng Z, Qiao Y (2019) Dynamic multi-scale filters for semantic segmentation. In:Proceedings of the IEEE/CVF international conference on computer vision, pp 3562–3572 He K, Gkioxari G, Dollár P, Girshick R(2017) Mask R-CNN. In:Proceedings of the IEEE international conference on computer vision, pp 2961–2969 He K, Zhang X, Ren S, Sun J(2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778 Huang Z, Wang X, Huang L, Huang C, Wei Y, Liu W (2019) Ccnet: Criss-cross attention for semantic segmentation. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 603–612 Islam MA, Jia S, Bruce NDB (2020) How much position information do convolutional neural networks encode? arXiv preprint arXiv:2001.08248 Kirillov A, He K, Girshick R, Rother C, Dollár P(2019) Panoptic segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 9404–9413 Lai Y-C, Chen B-A, Chen K-W, Si W-L, Yao C-Y, Zhang E (2016) Data-driven npr illustrations of natural flows in Chinese painting. IEEE Trans Vis Comput Graph 23(12):2535–2549 Lin TY, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117–2125 Lin TY, Dollár P, Girshick R, He K, Hariharan B, Belongie S(2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117–2125 Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988, Li H, Tao C, Zhu X, Wang X, Huang G, Dai J(2021) Auto seg-loss: searching metric surrogates for semantic segmentation. ArXiv, ArXiv:abs/2010.07930 Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 10012–10022 Liu S, Li F, Zhang H, Yang X, Qi X, Su H, Zhu J, Zhang L (2022) DAB-DETR: dynamic anchor boxes are better queries for DETR. In: International conference on learning representations Li X, Wang W, Hu X, Yang J(2019) Selective kernel networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 510–519 Loehr M (1964) The way of the brush: painting techniques of China and Japan. Harv J Asiat Stud 25:284–289 Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440 Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 Milletari F, Navab N, Ahmadi SA (2016) V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV), pp 565–571, IEEE MMSegmentation Contributors (2020) MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark. https://github.com/open-mmlab/mmsegmentation PaddlePaddle Contributors (2019) Paddleseg, end-to-end image segmentation kit based on paddlepaddle. https://github.com/PaddlePaddle/PaddleSeg Peng Z, Huang W, Gu S, Xie L, Wang Y, Jiao J, Ye Q (2021) Conformer: local features coupling global representations for visual recognition. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 367–376 Rezatofighi H, Tsoi N, Gwak JY, Sadeghian A, Reid I, Savarese S (2019) Generalized intersection over union: a metric and a loss for bounding box regression. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 658–666 Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18, pp 234–241. Springer Strudel R, Pinel RG, Laptev I, Schmid C(2021) Segmenter: transformer for semantic segmentation. In: ICCV, pp 7242–7252. IEEE Tang F, Dong W, Meng Y, Mei X, Huang F, Zhang X, Deussen O (2017) Animated construction of Chinese brush paintings. IEEE Trans Vis Comput Graph 24(12):3019–3031 Tian Z, Shen C, Chen H (2020) Conditional convolutions for instance segmentation. In: European conference on computer vision, pp 282–298. Springer Tong X-Y, Xia G-S, Qikai L, Shen H, Li S, You S, Zhang L (2020) Land-cover classification with high-resolution remote sensing images using transferable deep models. Remote Sens Environ 237:111322 Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst, 30 Wang T, Mo L, Vartanian O, Cant JS, Cupchik G (2015) An investigation of the neural substrates of mind wandering induced by viewing traditional Chinese landscape paintings. Front Hum Neurosci 8:1018 Wang J, Sun K, Cheng T, Jiang B, Deng C, Zhao Y, Liu D, Yadong M, Tan M, Wang X et al (2020) Deep high-resolution representation learning for visual recognition. IEEE Trans Pattern Anal Mach Intell 43(10):3349–3364 Wang X, Zhang R, Kong T, Li L, Shen C (2020) Solov2: dynamic and fast instance segmentation. Adv Neural Inf Process Syst 33:17721–17732 Wang X, Kong T, Shen C, Jiang Y, Li L (2020) Solo: segmenting objects by locations. In: European conference on computer vision, pp 649–665. Springer Wang G, Shen J, Yue M, Ma Y, Wu S (2022) A computational study of empty space ratios in Chinese landscape painting, pp 618–2011 Xiao T, Liu Y, Zhou B, Jiang Y, Sun J (2018) Unified perceptual parsing for scene understanding. In: Proceedings of the European conference on computer vision (ECCV), pp 418–434 Xiao T, Liu Y, Zhou B, Jiang Y, Sun J (2018) Unified perceptual parsing for scene understanding. In: Proceedings of the European conference on computer vision (ECCV), pp 418–434 Xie E, Wang W, Yu Z, Anandkumar A, Alvarez JM, Luo P (2021) Segformer: Simple and efficient design for semantic segmentation with transformers. Adv Neural Inf Process Syst 34:12077–12090 Xue A (2021) End-to-end chinese landscape painting creation using generative adversarial networks. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp 3863–3871 Xu J, Xiong Z, Bhattacharyya SP (2023) Pidnet: a real-time semantic segmentation network inspired by pid controllers. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 19529–1953 Yang D, Ye X, Guo B (2021) Application of multitask joint sparse representation algorithm in chinese painting image classification. Complexity Yin R, Monson E, Honig E, Daubechies I, Maggioni M (2016) Object recognition in art drawings: transfer of a neural network. In: 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 2299–2303. IEEE Yuan Y, Chen X, Wang J (2020) Object-contextual representations for semantic segmentation. In: European conference on computer vision, pp 173–190. Springer Zhang J, Zhou Y, Xia K, Jiang Y, Liu Y (2020) A novel automatic image segmentation method for chinese literati paintings using multi-view fuzzy clustering technology. Multimedia Syst 26(1):37–51 Zhang W, Pang J, Chen K, Loy CC (2021) K-net: toward unified image segmentation. Adv Neural Inf Process Syst 34:10326–10338 Zhao H, Shi J, Qi X, Wang X, Jia J (2017) Pyramid scene parsing network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2881–2890 Zhou P, Li K, Wei W, Wang Z, Zhou M (2020) Fast generation method of 3d scene in Chinese landscape painting. Multimed Tools Appl 79(23):16441–16457 Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2019) Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans Med Imaging Zhou B, Zhao H, Puig X, Fidler S, Barriuso A, Torralba A (2017) Scene parsing through ade20k dataset. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 633–641