1. Home
  2. JUITA Vol. 11 No. 2, November 2023
  3. Aditya

Implementation of Convolutional Neural Network Method in Identifying Fashion Image

Christian Sri Kusuma Aditya, Vinna Rahmayanti Setyaning Nastiti, Qori Raditya Damayanti, Gian Bagus Sadewa

Abstract


The fashion industry has changed a lot over the years, which makes it hard for people to compare different kinds of fashion. To make it easier, different styles of clothing are tried out to find the exact and precise look desired. So, we opted to employ the Convolutional Neural Network (CNN) method for fashion classification. This approach represents one of the methodologies employed to utilize computers for the purpose of recognizing and categorizing items. The goal of this research is to see how well the Convolutional Neural Network method classifies the Fashion-MNIST dataset compared to other methods, models, and classification processes used in previous research. The information in this dataset is about different types of clothes and accessories. These items are divided into 10 categories, which include ankle boots, bags, coats, dresses, pullovers, sandals, shirts, sneakers, t-shirts, and trousers. The new classification method worked better than before on the test dataset. It had an accuracy value of 95. 92%, which is higher than in previous research. This research also uses a method called image data generator to make the Fashion MNIST image better. This method helps prevent too much focus on certain details and makes the results more accurate.


Keywords


Convolutional Neural Network; Fashion-MNIST; classification; augmentation

References


[1] Y. Zhong and S. Mitra, “The role of fashion retail buyers in China and the buyer decision-making process,” J. Fash. Mark. Manag. An Int. J., vol. 24, no. 4, pp. 631–649, 2020, doi: https://doi.org/10.1108/JFMM-03-2018-0033.

[2] M. Xin and Y. Wang, “Research on image classification model based on deep convolution neural network,” Eurasip J. Image Video Process., vol. 2019, no. 1, 2019, doi: 10.1186/s13640-019-0417-8.

[3] K. V. Madhavi, R. Tamilkodi, and K. J. Sudha, “An Innovative Method for Retrieving Relevant Images by Getting the Top-ranked Images First Using Interactive Genetic Algorithm,” Procedia Comput. Sci., vol. 79, pp. 254–261, 2016, doi: 10.1016/j.procs.2016.03.033.

[4] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90, 2017, doi: 10.1145/3065386.

[5] A. S. Henrique et al., “Classifying Garments from Fashion-MNIST Dataset Through CNNs,” Adv. Sci. Technol. Eng. Syst. J., vol. 6, no. 1, pp. 989–994, 2021, doi: 10.25046/aj0601109.

[6] S. Bhatnagar, D. Ghosal, and M. H. Kolekar, “Classification of fashion article images using convolutional neural networks,” 2017 4th Int. Conf. Image Inf. Process. ICIIP 2017, vol. 2018-Janua, no. 1, pp. 357–362, 2018, doi: 10.1109/ICIIP.2017.8313740.

[7] K. Yamaguchi, M. H. Kiapour, L. E. Ortiz, and T. L. Berg, “Parsing clothing in fashion photographs,” in 2012 IEEE Conference on Computer vision and pattern recognition, 2012, pp. 3570–3577, doi: 10.1109/CVPR.2012.6248101.

[8] H. Chen, Z. J. Xu, Z. Q. Liu, and S. C. Zhu, “Composite templates for cloth modeling and sketching,” in 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), 2006, vol. 1, pp. 943–950, doi: 10.1109/CVPR.2006.81.

[9] L. Bossard, M. Dantone, C. Leistner, C. Wengert, T. Quack, and L. Van Gool, “Apparel classification with style,” in Computer Vision–ACCV 2012: 11th Asian Conference on Computer Vision, Daejeon, Korea, November 5-9, 2012, Revised Selected Papers, Part IV 11, 2013, pp. 321–335, doi: https://doi.org/10.1007/978-3-642-37447-0_25.

[10] K. V. Greeshma et al., “Classification of Garments from Fashion MNIST Dataset Using CNN LeNet-5 Architecture,” Proc. 2020 Int. Conf. Innov. Trends Commun. Comput. Eng. ITCE 2020, vol. 8, no. 2019, pp. 357–362, 2019, doi: 10.1109/ICIIP.2017.8313740, doi: 10.1109/ICIIP.2017.8313740..

[11] K. V. Greeshma and K. Sreekumar, “Fashion-MNIST classification based on HOG feature descriptor using SVM,” Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 5, pp. 960–962, 2019, Retrieval Number: E3075038519/19©BEIESP.

[12] K. V. Greeshma and K. Sreekumar, “Hyperparameter optimization and regularization on fashion-MNIST classification,” Int. J. Recent Technol. Eng., vol. 8, no. 2, pp. 3713–3719, 2019, doi: 10.35940/ijrte.B3092.078219.

[13] M. Kayed, A. Anter, and H. Mohamed, “Classification of Garments from Fashion MNIST Dataset Using CNN LeNet-5 Architecture,” Proc. 2020 Int. Conf. Innov. Trends Commun. Comput. Eng. ITCE 2020, no. June, pp. 238–243, 2020, doi: 10.1109/ITCE48509.2020.9047776.

[14] C. Duan, P. Yin, Y. Zhi, and X. Li, “Image Classification of Fashion-mnist Data Set Based on VGG Network,” Proc. 2019 2nd Int. Conf. Inf. Sci. Electron. Technol. (ISET 2019). Int. Informatiz. Eng. Assoc. Comput. Sci. Electron. Technol. Int. Soc., vol. 19, no. Iset, pp. 18–21, 2019.

[15] G. Ngo, V. Gaurav, and T. Shibata, “Clothing extremity identification using convolutional neural network regressor,” 2018 Jt. 7th Int. Conf. Informatics, Electron. Vis. 2nd Int. Conf. Imaging, Vis. Pattern Recognition, ICIEV-IVPR 2018, pp. 436–441, 2019, doi: 10.1109/ICIEV.2018.8641046.

[16] D. Alamsyah and D. Pratama, “Implementasi Convolutional Neural Networks (CNN) untuk Klasifikasi Ekspresi Citra Wajah pada FER-2013 Dataset,” J. Teknol. Inf., vol. 4, no. 2, pp. 350–355, 2020, doi:https://doi.org/10.36294/jurti.v4i2.1714.

[17] W. M. Pradnya D and A. P. Kusumaningtyas, “Analisis Pengaruh Data Augmentasi Pada Klasifikasi Bumbu Dapur Menggunakan Convolutional Neural Network,” J. Media Inform. Budidarma, vol. 6, no. 4, p. 2022, 2022, doi: 10.30865/mib.v6i4.4201.

[18] H. Xiao, K. Rasul, and R. Vollgraf, “Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms,” pp. 1–6, 2017, [Online]. Available: http://arxiv.org/abs/1708.07747.

[19] E. Rasywir, R. Sinaga, and Y. Pratama, “Analisis dan Implementasi Diagnosis Penyakit Sawit dengan Metode Convolutional Neural Network (CNN),” J. Parad. Ubsi, vol. 22, no. 2, pp. 117–123, 2020, doi: https://doi.org/10.31294/p.v21i2.

[20] A. Taherkhani, G. Cosma, and T. M. McGinnity, “AdaBoost-CNN: An adaptive boosting algorithm for convolutional neural networks to classify multi-class imbalanced datasets using transfer learning,” Neurocomputing, vol. 404, pp. 351–366, 2020, doi: 10.1016/j.neucom.2020.03.064.

[21] G. A. Sandag, J. Waworundeng, U. Klabat, J. A. Mononutu, and A. M. Utara, “Identifikasi Foto Fashion Dengan Menggunakan Convolutional Neural Network (CNN) Identify Fashion Images Using Convolutional Neural Network (CNN),” vol. 7, no. 2, pp. 305–314, 2021, doi: https://doi.org/10.31154/cogito.v7i2.340.305-314.


Full Text: PDF

DOI: 10.30595/juita.v11i2.17372

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN: 2579-8901