Cloth Pattern Recognition Using Machine Learning and Neural Network
DOI:
https://doi.org/10.56532/mjsat.v2i1.32Keywords:
Clothing Pattern Recognition, Radon Transform, Gray level co-occurance matrix, KNN, SVM, Neural NetworkAbstract
Visually impaired people face a lot of challenges while choosing clothes with complex patterns and colors. Rotation, scaling and variation in the light makes the cloth recognition problem a challenging task. An automatic cloth pattern recognition technique to classify the patterns into four classes namely plaid, striped, irregular and Patternless is developed using image processing, machine learning and deep learning concepts in this work. MATLAB is used as the simulation tool of choice. Color classification is done with the help of Hue Saturation Intensity (HSI) color model. To recognize clothing patterns, global and local features are extracted. Features extracted include Radon signatures and Grey Level Co-occurrence matrix. Pattern recognition has been done with the help of machine algorithms such as KNN, SVM, and deep learning networks such as AlexNet, GoogleNet, VGG-16 and VGG-19. To evaluate the effectiveness of the algorithms, CCNY Clothing Pattern data-set has been used. The maximum accuracy of 97.9% was obtained using the VGG-19 deep neural network.
References
Di, Wang. "A comparative research on clothing images classification based on neural network models." In 2020 IEEE 2nd International Conference on Civil Aviation Safety and Information Technology (ICCASIT, pp. 495-499. IEEE, 2020.
Liu, Yuanjun, Gaofeng Luo, and Feng Dong. "Convolutional Network Model using Hierarchical Prediction and its Application in Clothing Image Classification." In 2019 3rd International Conference on Data Science and Business Analytics (ICDSBA), pp. 157-160. IEEE, 2019.
Rao, AV Subba, H. C. Hadimani, and Srinivasarao Udara. "An Identification of Automatic Clothing Pattern and Color Recognition through Voice Recognition for Visually impaired people." Journal of Critical Reviews 7, no. 16 (2020): 2488-2495.
Zhang, Yuwei, Peng Zhang, Chun Yuan, and Zhi Wang. "Texture and Shape Biased Two-Stream Networks for Clothing Classification and Attribute Recognition." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13538-13547. 2020.
Xiaodong Yang,Shuai Yuan and YingLiTian, “Assistive Clothing Pattern Recognition for Visually Impaired People” ,in IEEE transactions on Human-Machine Systems, Vol. 44, no. 2, April 2014
S. Yuan, Y. Tian, and A. Arditi, “Clothes matching for visually impaired persons,” J.Technol. Disability, vol. 23, no. 2, pp. 75–85, 2011.
Masao Miyake, YoshitsuguManabe, Yuki Uranishi, MasatakaImura and Osamu Oshiro, “Voice Response System of Color and Pattern on Clothes for Visually Handicapped Person”, in35th Annual International Conference of the IEEE EMBS Osaka, Japan, 3 - 7 July, 2013
Xiaodong Yang, Shuai Yuan and YingLiTian, “Recognizing clothes patterns for blind people by confidence margin-based feature combination,” in Proc. Int. ACM Conf. Multimedia, 2011, pp. 1097–1100
N DurgaRao,, Dr. G Sudhavani, P Balakrishna,, Dr. K Gouthami,” Cloth Pattern Recognition With Four Features(RSSM)”,in 2015 International Symposium on Advanced Computing and Communication (ISACC)
Gandhi, Omkar, and Sanskar Dendage. "Cloth And Pattern Recognition for Visually Impaired People." (2019).
Stearns, Lee, Leah Findlater, and Jon E. Froehlich. "Applying transfer learning to recognize clothing patterns using a finger-mounted camera." In Proceedings of the 20th International ACM SIGACCESS Conference on Computers and Accessibility, pp. 349-351. 2018.
Menakadevi, N., T. Nagarjuna, and K. Nehru. "Pattern Recognition for Visually Impaired People Using Radon Signature." (2020).
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Copyright (c) 2022 Sreemathy R, M.P Turuk, Soumya Khurana
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