Identification of Miao Embroidery in Southeast Guizhou Province of China Based on Convolution Neural Network

• Autorzy: Zhang Chune , Wu Song , Chen Jianhui

Identyfikatory

DOI

10.2478/aut-2020-0063

• Języki publikacji: EN

Abstrakty

EN

Miao embroidery of the southeast area of Guizhou province in China is a kind of precious intangible cultural heritage, as well as national costume handcrafts and textiles, with delicate patterns that require exquisite workmanship. There are various skills to make Miao embroidery; therefore, it is difficult to distinguish the categories of Miao embroidery if there is a lack of sufficient knowledge about it. Furthermore, the identification of Miao embroidery based on existing manual methods is relatively low and inefficient. Thus, in this work, a novel method is proposed to identify different categories of Miao embroidery by using deep convolutional neural networks (CNNs). Firstly, we established a Miao embroidery image database and manually assigned an accurate category label of Miao embroidery to each image. Then, a pre-trained deep CNN model is fine-tuned based on the established database to learning a more robust deep model to identify the types of Miao embroidery. To evaluate the performance of the proposed deep model for the application of Miao embroidery categories recognition, three traditional non-deep methods, that is, bag-of-words (BoW), Fisher vector (FV), and vector of locally aggregated descriptors (VLAD) are employed and compared in the experiment. The experimental results demonstrate that the proposed deep CNN model outperforms the compared three non-deep methods and achieved a recognition accuracy of 98.88%. To our best knowledge, this is the first one to apply CNNs on the application of Miao embroidery categories recognition. Moreover, the effectiveness of our proposed method illustrates that the CNN-based approach might be a promising strategy for the discrimination and identification of different other embroidery and national costume patterns.

Słowa kluczowe

EN

southeast Guizhou; China; Miao embroidery identification; deep CNNs; convolutional neural networks

PL

południowo-wschodni Guizhou; Chiny; identyfikacja haftu Miao; konwolucyjne sieci neuronowe

• Wydawca: Lodz University of Technology, Faculty of Material Technologies and Textile Design
• Czasopismo: Autex Research Journal
• Rocznik: 2021
• Tom: Vol. 21, no. 2
• Strony: 198--206
• Opis fizyczny: Bibliogr. 37 poz.

Twórcy

autor

Zhang Chune

• Fashion & Art Design Institute, Donghua University, Shanghai,200051, China
• College of Sericulture, Textile and Biomass Sciences, Southwest University, Chongqing, 400715, China

autor

Wu Song

• College of Computer and Information Science, Southwest University, Chongqing, 400715, China

autor

Chen Jianhui

• Fashion & Art Design Institute, Donghua University, Shanghai,200051, China

Bibliografia

• [1] Liu Bingbing, Yuan Yan. (2019). Try to talk about Historical Memory of Miao Dress Design Symbols. Guizhou Ethnic Studies,40:79–82.
• [2] Li Ming. (2019). Discission on the Artistic Forms and Causes of Miao Embroidery Crafts in Southeast Guihou. Folk Art, 06:90–94.
• [3] Zhang Lei, Qin Ziyi. (2019). China National Dyeing and Weaving Intangible Culture Heritage List. Fashion Guide, 07(2):13–23.
• [4] Yang Shengfeng. (2018). A Brief Analysis of the Development Status of Miao Embroidery Industry in Southeast Guizhou. Today's Massmedia, 08:175–176.
• [5] Qi Yuying, Zhang Xiao. (2018). On the Development of Miao Embroidery Industrialization in Southeast Guizhou From the Perspective of Production Protection. China Collective Economy, 01:27–29.
• [6] Shang Huifang, Mou Xiaomei, Wang Jiaoyan. (2018). Study on the Present Situation and Countermeasures of Miao Embroidery in Guizhou. Journal of Qiannan Normal University for Nationalities, 38(3):122–124.
• [7] TOMOKO Torimaru. One Needle, One Thread. (2011). Guizhou Miao (Hmong) Embroidery and Fabric Piece work from Guizhou, China. China Textile & Apparel Press(Beijing).
• [8] Zhang Y, Jin R, Zhou Z H. (2010). Understanding bag-of-words model: a statistical framework [J]. International Journal of Machine Learning & Cybernetics, 1(1–4):43–52.
• [9] Gosselin P H, Murray N, Jégou H, et al. (2014). Revisiting the Fisher vector for fine-grained classification. Pattern Recognition Letters,49:92–98.
• [10] He Yunfeng, Zhou Ling, Yu Junqing, Xu Tao, Guan Tao. (201). Image Retrieval Based on Locally Features Aggregating [J]. Chinese Journal of Computers, 34(11):2224–2233.
• [11] Chang L, Deng X M, Zhou M Q, et al. (2016). Convolutional Neural networks in Image understanding. Acta Automatica Sinica.
• [12] Artzai P, Maximilian S, Aitor A G, Patrick M, Amaia O B, Jone E. (2019). Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions. Computers and Electronics in Agriculture, 167:105093.
• [13] Kattenborn T, Eichel J, Fassnacht F E. (2019). Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery. Scientific Reports. 9:17656.
• [14] Jia Xiaojun, Ye Lihua, Deng Hongtao, Liu Zihao, Lu Fengjie. (2020). Elements classification of vein patterns using convolutional neural networks for blue calico [J]. Journal of Textile Research,41(1).110–117.
• [15] Sheng Jiachuan, Chen Yaqi, Wang Jun, Li Jiang. (2020). Chinese paintings sentiment recognition via CNN optimization with human cognition [J]. Pattern Recognition and Artificial Intelligence, 33(2):141–149.
• [16] Li Rongrui, Shi Lin, Zhao Wei. (2019). Minority headdress recognition based on Convolutional Neural Network [J]. Electronic Sci.&Tech, 32(2):51–55.
• [17] Ai Hu, Li Fei. (2019). Identification of Guizhou dialect based on improved convolutional neural network [J]. Modern Information Technology, 3(1):5–10.
• [18] Wu Huan, Ding Xiaojun, Li Qinman, Du Lei, Zou Fengyuan. (2019). Classification of women's trousers silhouette using convolution neural network CaffeNet model [J]. Journal of Textile Research, 40(4):117–121.
• [19] Wang Fei, Jin Xiangyu. (2017). Identification of cashmere and wool based on convolutional neuron networks and deep learning theory [J]. Journal of Textile Research, 38(12):150–156.
• [20] Liu Zhengdong, Liu yihan, Wang Shouren. (2019). Depth learning method for suit detection in images [J]. Journal of Textile Research, 40(04):158–164.
• [21] He Xiaoyun, Wei Ping, Zhang Lin, Deng Binyou, Pan Yunfeng, Su Zhenwei. (2018). Detection method of foreign fibers in seed cotton based on deep-learning. Journal of Textile Research, 39(6):131–135.
• [22] Wang Wenwen, Gao Chang, Liu Jihong. (2018). Position recognition of spinning yarn breakage based on convolution neural network. Journal of Textile Research, 39(6):136–141.
• [23] Wang Shanna, Zhang Huaxiong, Kang Feng. (2018). Emotion classification of necktie pattern based on convolution neural network. Journal of Textile Research, 39(8):117–123.
• [24] Corona, E; Alenya, G; Gabas, A; Torras, C. (2018). Active garment recognition and target grasping point detection using deep learning. Pattern Recognition, 74(2):629–641.
• [25] Zhao Yudi, Hao Kuangrong, He Haibo, Tang Xuesong, Bei Bing. (2020). A visual long-short-term memory based integrated CNN model for fabric defect image classification. Neurocomputing, (380)03:259–270.
• [26] Jing Jun-Feng, Ma Hao, Zhang Huanhuan. (2019). Automatic fabric defect detection using a deep convolutional neural network. Coloration Technology, 135(6):213–223.
• [27] Szegedy C, Ioffe S, Vanhoucke V, et al. (2017). Inception-v4, inception-ResNet and the impact of residual connections on learning. 31st AAAI Conference on Artificial Intelligence, AAAI 2017:4278–4284.
• [28] Szegedy, C., Com, S.G. (2017). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. arXiv,37.
• [29] Szegedy, C., Vanhoucke, V., Shlens, J. (2016). Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE conference on Computer Vision and Pattern Recognition,2818–2826.
• [30] Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A. (2017). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. In: AAAI, 4:12.
• [31] Kuang H L, Liu C R, Chan L L, Yan H. (2018). Multi-class fruit detection based on image region selection and improved object proposals. Neorocomputing. 283:241–255.
• [32] Mohanty, S.P., Hughes, D.P., Salathé, M. (2016). Using deep learning for image-based plant disease detection [J]. Frontiers in Plant Science, 7 (9): 1–7.
• [33] http://pytorch.org/.
• [34] http://opencv.org/.
• [35] http://python.org/.
• [36] Li Shengwang, Han Qian. (2018). Image Processing Technology Based on Deep Learning. Digital Technology &Application,36(09):5–66.
• [37] Cheng Deng, Zhaojia Chen, Xianglong Liu, Xinbo Gao, Dacheng Tao. (2018). Triplet-Based Deep Hashing Network for Cross-Modal Retrieval. IEEE Transactions on Image Processing, 27 (8), 3893–3903.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).