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Analysis of Clothing Image Classification Models: A Comparison Study between Traditional Machine Learning and Deep Learning Models Cover

Analysis of Clothing Image Classification Models: A Comparison Study between Traditional Machine Learning and Deep Learning Models

Fibres & Textiles in Eastern Europe
Volume 30 (2022): Issue 5 (October 2022)
By: Jun Xu,  Yumeng Wei,  Aichun Wang,  Heng Zhao and  Damien Lefloch  
Open Access
|Dec 2022

References

  1. Jing Y, Liu D, Kislyuk D, Zhai A, Xu J, Donahue J, Tavel S. Visual search at Pinterest, In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '15. Association for Computing Machinery, New York, NY, USA, 2015; pp. 1889–1898.
    Search in Google ScholarBack to article
  2. Gao Z, Han L. Clothing image classification based on random erasing and residual network. Journal of Physics: Conference Series 2020; Vol. 1634 No. 1, pp. 012136.
    Search in Google ScholarBack to article
  3. Feng Y, Du C, Hua A, Jiang M, Wei X, Peng T, Hu X. EnCaps: Clothing Image Classification Based on Enhanced Capsule Network. Applied Sciences 2021; Vol. 11 No. 22, pp.11024.
    Search in Google ScholarBack to article
  4. Kiapour MH, Han X, Lazebnik S, Berg AC, Berg TL. Where to buy it: Matching street clothing photos in online shops, in: IEEE International Conference on Computer Vision (ICCV), IEEE, Santiago, Chile 2015; pp. 3343–3351.
    Search in Google ScholarBack to article
  5. Liu KH, Chen TY, Chen CS. MVC: A Dataset for View-Invariant Clothing Retrieval and Attribute Prediction, in: Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval (ICMR ‘16), Association for Computing Machinery, New York, NY, USA 2016; pp. 313–316.
    Search in Google ScholarBack to article
  6. Deng LL. Pre-detection Technology of Clothing Image Segmentation Based on GrabCut Algorithm. Wireless Pers Commun 2018; Vol. 102 No. 2, pp. 599–610.
    Search in Google ScholarBack to article
  7. Yue X, Zhang C, Fujita H, Lv Y. Clothing fashion style recognition with design issue graph. Appl Intell 2020 ; Vol 51 No. 10, pp. 1–13.
    Search in Google ScholarBack to article
  8. Li Y, He Z, Wang S, Huang W. Multideep Feature Fusion Algorithm for Clothing Style Recognition. Wirel Commun Mob Com 2021; Vol. 2021 No. 4, pp. 1–14.
    Search in Google ScholarBack to article
  9. Kashilani D, Damahe LB, Thakur NV. An Overview of Image Recognition and Retrieval of Clothing items, in: IEEE International Conference on Research in Intelligent and Computing in Engineering (RICE), IEEE, San Salvador, El Salvador 2018; pp. 1–6.
    Search in Google ScholarBack to article
  10. Donati L, Iotti E, Mordonini G, Prati A. Fashion Product Classification through Deep Learning and Computer Vision. Applied Sciences, 2019; Vol. 9 No. 7, pp. 1385.
    Search in Google ScholarBack to article
  11. Stricker M, Orengo M. Storage and Retrieval for Image and Video Databases III. SPIE International Society for Optics and Photonics 1995; Vol. 1995, pp. 381–392.
    Search in Google ScholarBack to article
  12. Hu MK. Visual pattern recognition by moment invariants. Information Theory, IRE Transactions on 1962; Vol. 8 No. 2, pp. 179–187.
    Search in Google ScholarBack to article
  13. Li Y, Xu S, Luo X, Lin S. A new algorithm for product image search based on salient edge characterization. Journal of the Association for Information Science & Technology 2014 ; Vol. 65 No. 12, pp. 2534–2551.
    Search in Google ScholarBack to article
  14. Lowe DG. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 2004; Vol. 60 No. 2, pp. 91–110.
    Search in Google ScholarBack to article
  15. Li KL, Liu ZD. Clothing Information System Design Based on Images Retrieval, in: International Conference on Advanced Educational Technology and Information Engineering (AETIE 2015), Computer Information Centre, Beijing Institute of Fashion Technology 2015; Vol. 2015, pp. 677–683.
    Search in Google ScholarBack to article
  16. Dalal N, Triggs B. Histograms of Oriented Gradients for Human Detection, in: IEEE Computer Society Conference on Computer Vision & Pattern Recognition (CVPR'05), IEEE, San Diego, CA, USA 2005; pp. 886–893.
    Search in Google ScholarBack to article
  17. Thewsuwan S, Horio K. Preprocessing Techniques Based on LBP and Gabor Filters for Clothing Classification, in: 2016 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), IEEE, Phuket, Thailand, Vol. 2016, pp. 1–6.
    Search in Google ScholarBack to article
  18. Sha D, Wang D, Zhou X, F Shi, Ge Y. An Approach for Clothing Recommendation Based on Multiple Image Attributes, in: Cui, B., Zhang, N., Xu, J., Lian, X., & Liu, D. (Eds.), International Conference on Web-Age Information Management, Springer, Cham 2016; pp. 272–285.
    Search in Google ScholarBack to article
  19. Surakarin W, Chongstitvatana P. Predicting Types of Clothing Using SURF and LDP based on Bag of Features, in: 2015 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), IEEE, Hua Hin, Thailand 2015; pp. 1–5.
    Search in Google ScholarBack to article
  20. Li JQ, Shi WM, Yang DH. Clothing Image Classification with a Dragonfly Algorithm Optimised Online Sequential Extreme Learning Machine. Fibres Text East Eur 2021 ; Vol. 29 No. 3, pp. 90–95.
    Search in Google ScholarBack to article
  21. Tabik S, Peralta D, Herrera-Poyatos A, Herrera F. A snapshot of image preprocessing for convolutional neural networks: case study of MNIST. International Journal of Computational Intelligence Systems 2017 ; Vol. 10 No. 1, pp. 555.
    Search in Google ScholarBack to article
  22. Bui HM, Lech M, Cheng E, Neville K, Burnett IS. Object Recognition Using Deep Convolutional Features Transformed by a Recursive Network Structure. IEEE Access 2016 ; Vol. 4, pp. 10059–10066.
    Search in Google ScholarBack to article
  23. Tang P, Wang H, Kwong S. G-MS2F: GoogLeNet based multi-stage feature fusion of deep CNN for scene recognition. Neurocomputing. Neurocomputing 2017 ; Vol. 225 FEB.15, pp. 188–197.
    Search in Google ScholarBack to article
  24. Ghazi MM, Yanikoglu B, Aptoula E. Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing 2017 ; Vol. 235 APR.26, pp. 228–235.
    Search in Google ScholarBack to article
  25. Han K, Wang Y, Tian Q, Guo J, Xu C. GhostNet: More Features From Cheap Operations, in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Seattle, WA, USA 2020 ; pp. 1577–1586.
    Search in Google ScholarBack to article
  26. Lao B, Jagadeesh K. Convolutional Neural Networks for Fashion Classification and Object Detection. 2015.
    Search in Google ScholarBack to article
  27. Dong C, Shi Y, Tao R. Convolutional Neural Networks for Clothing Image Style Recognition. in: Proceedings of 2018 International Conference on Computational, Modeling, Simulation and Mathematical Statistics (CMSMS), Xi'an, China 2018, pp. 608–613.
    Search in Google ScholarBack to article
  28. Xiang J, Dong T, Pan R,, Gao W. Clothing Attribute Recognition Based on RCNN Framework Using L-Softmax Loss. IEEE Access 2020 ; Vol. 8, pp. 48299–48313.
    Search in Google ScholarBack to article
  29. Di W. A comparative research on clothing images classification based on neural network models, in: IEEE 2nd International Conference on Civil Aviation Safety and Information Technology (ICCASIT), IEEE, Weihai, China 2020; pp. 495–499.
    Search in Google ScholarBack to article
  30. Bossard L, Dantone M, Leistner C, Wengert C, Quack T, Gool LV. Apparel Classification with Style, in: Lee, K.M., Matsushita, Y., Rehg, J. M., Hu, Z.Y. (Ed.s), Asian conference on computer vision (ACCV), Springer-Verlag, Berlin, Heidelberg 2012; pp. 321–335.
    Search in Google ScholarBack to article
  31. Huo P, Wang Y, Liu Q. A part-based and feature fusion method for clothing classification, in: Pacific Rim Conference on Multimedia, Springer, Xi'an 2016; pp. 231–241.
    Search in Google ScholarBack to article
  32. Liu S, Feng J, Domokos C, Xu H, Huang J, Hu Z, Yan S. Fashion Parsing with Weak Colour-category Labels. IEEE Transactions on Multimedia 2014; Vol. 16 No. 1, pp. 253–265.
    Search in Google ScholarBack to article
  33. Liu Z, Luo P, Qiu S, Wang X, Tang X. DeepFashion: Powering ro-bust clothes recognition and retrieval with rich annotations, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Las Vegas, NV, USA 2016; pp. 1096–1104.
    Search in Google ScholarBack to article
  34. Wang J, Yang Y, Mao J, Huang Z, Huang C, Xu W. CNN-RNN: A unified framework for multi-label image classification, in: IEEE conference on computer vision and pattern recognition (CVPR), IEEE, Las Vegas, NV, USA 2016; pp. 2285–2294.
    Search in Google ScholarBack to article
  35. Nawaz M, Hasan R, Hasan MA, Hassan M, Rahman RM. Automatic Categorization of Traditional Clothing Using Convolutional Neural Network, in: IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS), IEEE, Singapore 2018; pp. 98–103.
    Search in Google ScholarBack to article
  36. Cychnerski J, Brzeski A, Boguszewski A, Marmolowski M, Trojanowicz M. Clothes detection and classification using convolutional neural networks, in: IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), IEEE, Limassol, Cyprus 2017; pp. 1–8.
    Search in Google ScholarBack to article
  37. Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5MB model size. Under Review as a Conference Paper at ICLR. 2016.
    Search in Google ScholarBack to article
  38. Zhang X, Song C, Yang Y, Zhang Z, Zou Q. Deep learning based human body segmentation for clothing fashion classification”, in: Chinese Automation Congress (CAC), Shanghai, China 2020; Vol. 2020 Nov. 6–8, pp. 7544–7549.
    Search in Google ScholarBack to article
  39. Rohrmanstorfer S, Komarov M, Modritscher F. Image Classification for the Automatic Feature Extraction in Human Worn Fashion Data. Mathematics 2021 ; Vol. 9 No. 6, pp. 624.
    Search in Google ScholarBack to article
  40. Hodecker A, Fernandes A, Steffens A, Crocker P, Leithardt V. Clothing Classification Using Convolutional Neural Networks, in: 15th IEEE Iberian Conference on Information Systems and Technologies (CISTI), IEEE, Seville, Spain 2020; pp. 1–6.
    Search in Google ScholarBack to article
  41. Basavanhally AN, Ganesan S, Agner S, Monaco JP, Feldman MD, Tomaszewski JE, Bhanot G, Madabhushi, A. Computerized image-based detection and grading of lymphocytic infiltration in HER2-breast cancer histopathology. IEEE Transactions on Biomedical Engineering 2010; Vol. 57 No. 3, pp. 642–653.
    Search in Google ScholarBack to article
  42. Adrian R. Multi-label classification with Keras, available at: https://www.pyimagesearch.com/2018/05/07/multi-label-classification-with-keras/ (accessed 7 May 2018)
    Search in Google ScholarBack to article
  43. Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition. Computer Science 2014; pp. 1–14.
    Search in Google ScholarBack to article
  44. Xiao H, Rasul K, Vollgraf R. Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms”. 2017.
    Search in Google ScholarBack to article
  45. Simo-Serra E, Ishikawa H. Fashion Style in 128 Floats: Joint Ranking and Classification Using Weak Data for Feature Extraction, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Las Vegas, NV, USA 2016; pp. 298–307.
    Search in Google ScholarBack to article
  46. Wang L, Guo S, Huang W, Xiong Y, Qiao Y. Knowledge Guided Disambiguation for Large-Scale Scene Classification with Multi-Resolution CNNs, in: IEEE Transactions on Image Processing, IEEE 2017; Vol. 26 No. 4, pp. 2055–2068.
    Search in Google ScholarBack to article
DOI: https://doi.org/10.2478/ftee-2022-0046 | Journal eISSN: 2300-7354 | Journal ISSN: 1230-3666
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Language: English
Page range: 66 - 78
Published on: Dec 22, 2022
Published by: Łukasiewicz Research Network, Institute of Biopolymers and Chemical Fibres
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open
Keywords:
E-commerce,
clothing image classification,
traditional machine learning,
CNN,
HOG+SVM,
small VGG network
Related subjects:
Business and economics,
Business management,
Business informatics,
Process management,
Industrial chemistry,
Cellulose, paper and textiles,
Polymer science and technology,
Materials sciences,
Biomaterials and natural materials

© 2022 Jun Xu, Yumeng Wei, Aichun Wang, Heng Zhao, Damien Lefloch, published by Łukasiewicz Research Network, Institute of Biopolymers and Chemical Fibres
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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