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STD-net: saree texture detection via deep learning framework for E-commerce applications

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Abstract

In this modern world, people move fast and most people are very busy in their daily scheduled lives. In such a scenario, E-commerce online shopping is a great time-saver. In general, ladies clothing has numerous characteristics that are hard to designate such as texture, shape, color, print, and length. Moreover, accurate extraction of product features is critical in the analysis of fashion images for product search, and texture detection based on the query images remains a more challenging task. To overcome the aforementioned challenges, a novel deep learning-based saree texture detection network (STD-Net) has been proposed for the rapid classification of saree tactile textures based on the user query. The research work is conducted in three phases: (1) Indian Saree Dataset creation and Pre-processing phase (2) Patch generation phase (3) Texture detection of the query image. Initially, the input images are denoised using SCRAB (scalable range-based adaptive bilateral filter). Afterward, a region-based convolutional neural network (RCNN) is used for segmenting the region of interest viz, the field part of a saree into patches with the augmented and annotated dataset of sarees. Finally, the Modified EfficientNet-B3 which is integrated with the squeeze and excitation attention (SEA) module is used to classify the texture of the sarees. The experimental results disclose that the proposed STD-Net attains a better testing accuracy of 99.1% for the texture classification of saree images.

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References

  1. https://www.livafluidfashion.com/pdf/India_Saree_Trends.pdf.

  2. https://www.dollsofindia.com/library/saree/.

  3. Stout, E.E.: Introduction to textiles. 3rd ed. New York, John Wiley and Sons, Inc. 363(1970).

  4. Potter, M.D., Corbman. B.P.: Textiles: Fibre to fabric. New York, Macmillan Hill Co. 342 (1967).

  5. Hollen, N., Saddler, J.: Textiles. 4th edition. New York, Macmillan Company. 219 (1973).

  6. Tortora, P.G.: Understanding Textiles. New York, Macmillan Publishing Co., Inc. 413 (1978).

  7. Weilong, L.: Interactive clothing image segmentation based on superpixels and graph cuts. in 2013 international conference on computer sciences and applications, 659–662 (2013). IEEE. DOI: https://doi.org/10.1109/CSA.2013.160

  8. Zhang, J., Liu L., Huang, D., Fu, X., Huang, Q.: Clothing Co-Segmentation Based on HOG Features and E-SVM Classifier. In 2016 6th International Conference on Digital Home (ICDH) 16–19 (2016). IEEE. DOI: https://doi.org/10.1109/ICDH.2016.013

  9. Deng, L.L.: Pre-detection technology of clothing image segmentation based on GrabCut algorithm. Wireless Pers. Commun. 102(2), 599–610 (2018). https://doi.org/10.1007/s11277-017-5050-1

    Article  Google Scholar 

  10. Zhang, J., Liu, C.: A study of a clothing image segmentation method in complex conditions using a features fusion model. Automatika 61(1), 150–157 (2020). https://doi.org/10.1080/00051144.2019.1691835

    Article  Google Scholar 

  11. Mendoza, A., Trullo, R., Wielhorski, Y.: Descriptive modeling of textiles using FE simulations and deep learning. Compos. Sci. Technol. 213, 108897 (2021). https://doi.org/10.1016/j.compscitech.2021.108897

    Article  Google Scholar 

  12. Yingheng, X., Yueqi, Z.: Multiple Attention Mechanism Neural Network in Garment Image Segmentation. In 2020 International Conference on Computational Science and Computational Intelligence (CSCI) 1677- 1683 (2020). IEEE. DOI: https://doi.org/10.1109/CSCI51800.2020.00309

  13. Pawening, R.E., Dijaya, R., Brian, T., Suciati, N.: Classification of textile image using support vector machine with textural feature. In 2015 International Conference on Information & Communication Technology and Systems (ICTS) 119–122 (2015). IEEE. DOI: https://doi.org/10.1109/ICTS.2015.7379883

  14. Greeshma, K.V., Gripsy, J.V.: Image classification using HOG and LBP feature descriptors with SVM and CNN. Int J Eng Res Technol 8(4), 1–4 (2020)

    Google Scholar 

  15. Dhongade, M.: Clothing pattern recognition for blind using surf and combined glcm, wavelet. Int J Sci Res (IJSR) 2013, 2319–7064 (2015)

    Google Scholar 

  16. Leung, T., Malik, J.: Representing and recognizing the visual appearance of materials using three-dimensional textons. Int J Comput Vis Fine Grain Mater Classification Using Micro Geometry Reflectance 43(1), 29–44 (2001)

    Google Scholar 

  17. Varma, M., Zisserman, A.: A statistical approach to texture classification from single images. Int. J. Comput. Vision 62(1), 61–81 (2005). https://doi.org/10.1007/s11263-005-4635-4

    Article  Google Scholar 

  18. Varma, M., Zisserman, A.: A statistical approach to material classification using image patch exemplars. IEEE Trans. Pattern Anal. Mach. Intell. 31(11), 2032–2047 (2009). https://doi.org/10.1109/TPAMI.2008.182

    Article  Google Scholar 

  19. Dana, K., Van-Ginneken, B., Nayar, S., Koenderink, J.: Reflectance and texture of real-world surfaces. ACM Trans. Graphics (TOG) 18(1), 1–34 (1999). https://doi.org/10.1145/300776.300778

    Article  Google Scholar 

  20. ALOT: www.science.uva.nl/∼mark/alot

  21. KTH-TIPS2: www.nada.kth.se/cvap/databases/kth-tips

  22. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. 248–255 (2009). DOI: https://doi.org/10.1109/CVPR.2009.5206848

  23. Sharan, L., Rosenholtz, R., Adelson, E.: Material perception: what can you see in a brief glance? J. Vis. 9(8), 784 (2009). https://doi.org/10.1167/9.8.784

    Article  Google Scholar 

  24. Nocentini, O., Kim, J., Bashir, M.Z., Cavallo, F.: Image classification using multiple convolutional neural networks on the fashion-MNIST dataset. Sens. 22(23), 9544 (2022). https://doi.org/10.3390/s22239544

    Article  Google Scholar 

  25. Vijayaraj, A., Raj, V., Jebakumar, R., Gururama Senthilvel, P., Kumar, N., Suresh Kumar, R., Dhanagopal, R.: Deep Learning Image Classification for Fashion Design. Wireless Commun. Mobile Comput. 2022 (2022).

  26. Karolia, A., Ladia, B.: Traditional textiles and costumes of Karbi and Biate tribes of Meghalaya (2012). http://nopr.niscpr.res.in/handle/123456789/13862

  27. Li, S., Liu, J., Song, Z.: Brain tumor segmentation based on region of interest-aided localization and segmentation U-Net. Int. J. Mach. Learn. Cybern. 13(9), 2435–2445 (2022)

    Article  Google Scholar 

  28. Alhichri, H., Alswayed, A.S., Bazi, Y., Ammour, N., Alajlan, N.A.: Classification of remote sensing images using EfficientNet-B3 CNN model with attention. IEEE access 9, 14078–14094 (2021)

    Article  Google Scholar 

  29. Atila, Ü., Uçar, M., Akyol, K., Uçar, E.: Plant leaf disease classification using EfficientNet deep learning model. Eco. Inform. 61, 101182 (2021)

    Article  Google Scholar 

  30. Taunyazov, T., Chua, Y., Gao, R., Soh, H. and Wu, Y.: Fast texture classification using tactile neural coding and spiking neural network. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 9890–9895). IEEE. (2020)

  31. Shin, S.Y., Jo, G., Wang, G.: A novel method for fashion clothing image classification based on deep learning. J. Inf. Commun. Technol. 22(1), 127–148 (2023)

    Google Scholar 

  32. Zhang, Y., Zhang, P., Yuan, C. and Wang, Z.: 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)

  33. Alotaibi, A.: A hybird framework based on autoencoder and deep neural networks for fashion image classification. International Journal of Advanced Computer Science and Applications, 11(12). (2020)

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Acknowledgements

The authors express their gratitude to the Thiagarajar College of Engineering (TCE) for supporting us to carry out this research work. Also, the financial support from TCE under the Thiagarajar Research Fellowship scheme (File.no: TRF/Jul-2022/01) is gratefully acknowledged.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Thiagarajar College of Engineering, Madurai, 625 015, India

    D. Karthika Priya, B. Sathya Bama & S. Mohamed Mansoor Roomi

  2. Department of Computer Science Engineering, Thiagarajar College of Engineering, Madurai, 625 015, India

    M. P. Ramkumar

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  1. D. Karthika Priya
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  3. M. P. Ramkumar
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  4. S. Mohamed Mansoor Roomi
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Contributions

The authors confirm their contribution to the paper as follows: study conception and design: DKP, BSB; Data collection: MPR; Analysis and interpretation of results: SMMR; Draft manuscript preparation: DKP, SMMR. All authors reviewed the results and approved the final version of the manuscript.

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Correspondence to D. Karthika Priya.

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Priya, D.K., Bama, B.S., Ramkumar, M.P. et al. STD-net: saree texture detection via deep learning framework for E-commerce applications. SIViP 18, 495–503 (2024). https://doi.org/10.1007/s11760-023-02757-y

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