Advertisement

Tiered Deep Similarity Search for Fashion

  • Dipu ManandharEmail author
  • Muhammet Bastan
  • Kim-Hui Yap
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11131)

Abstract

How similar are two fashion clothing? Fashion apparels demonstrate diverse visual concepts with their designs, styles and brands. Hence, there exist a hierarchy of similarities between fashion clothing, ranging from exact instance or brand to similar attributes, styles. An effective search method, thus, should be able to represent the tiers of similarities. In this paper, we present a deep learning based fashion search framework for learning the tiers of similarity. We propose a new attribute-guided metric learning (AGML) with multitask CNNs that jointly learns fashion attributes and image embeddings while taking category and brand information into account. The two tasks in the framework are linked with a guiding signal. The guiding signal, first, helps in mining informative training samples. Secondly, it helps in treating training samples by their importance to capture the tiers of similarity. We conduct experiments in a new BrandFashion dataset which is richly annotated at different granularities. Experimental results demonstrate that the proposed method is very effective in capturing a tiered similarity search space and outperforms the state-of-the-art fashion search methods.

Keywords

Fashion search Deep metric learning Multitask learning 

Notes

Acknowledgment

This research was carried out at the Rapid-Rich Object Search (ROSE) Lab at the Nanyang Technological University, Singapore. The ROSE Lab is supported by the Infocomm Media Development Authority, Singapore. We gratefully acknowledge the support of NVIDIA AI Technology Center for their donation of GPUs used for our research.

Supplementary material

478822_1_En_3_MOESM1_ESM.pdf (7.6 mb)
Supplementary material 1 (pdf 7826 KB)

References

  1. 1.
    Al-Halah, Z., Stiefelhagen, R., Grauman, K.: Fashion forward: forecasting visual style in fashion. In: IEEE International Conference on Computer Vision (ICCV), pp. 388–397. IEEE (2017)Google Scholar
  2. 2.
    Baldwin, C.: Online spending continues to increase thanks to fashion sector (2014). https://www.computerweekly.com/news/2240225386/Spend-online-continues-to-increase-thanks-to-fashion-sector
  3. 3.
    Bell, S., Bala, K.: Learning visual similarity for product design with convolutional neural networks. ACM Trans. Graph. (TOG) 34(4), 98 (2015)CrossRefGoogle Scholar
  4. 4.
    Bossard, L., Dantone, M., Leistner, C., Wengert, C., Quack, T., Van Gool, L.: Apparel classification with style. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012. LNCS, vol. 7727, pp. 321–335. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-37447-0_25CrossRefGoogle Scholar
  5. 5.
    Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., Shah, R.: Signature verification using a siamese time delay neural network. In: Advances in Neural Information Processing Systems, pp. 737–744 (1994)Google Scholar
  6. 6.
    Chechik, G., Sharma, V., Shalit, U., Bengio, S.: Large scale online learning of image similarity through ranking. J. Mach. Learn. Res. 11, 1109–1135 (2010)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Chen, H., Gallagher, A., Girod, B.: Describing clothing by semantic attributes. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7574, pp. 609–623. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33712-3_44CrossRefGoogle Scholar
  8. 8.
    Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 539–546 (2005)Google Scholar
  9. 9.
    Financial Times: online retail sales continue to soar (2018). https://www.ft.com/content/a8f5c780-f46d-11e7-a4c9-bbdefa4f210b
  10. 10.
    Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv:1703.07737 (2017)
  11. 11.
    Hu, J., Lu, J., Tan, Y.P.: Discriminative deep metric learning for face verification in the wild. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1875–1882 (2014)Google Scholar
  12. 12.
    Huang, J., Feris, R.S., Chen, Q., Yan, S.: Cross-domain image retrieval with a dual attribute-aware ranking network. In: International Conference on Computer Vision, pp. 1062–1070 (2015)Google Scholar
  13. 13.
    Jing, Y., et al.: Visual search at pinterest. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1889–1898. ACM (2015)Google Scholar
  14. 14.
    Khamis, S., Kuo, C.-H., Singh, V.K., Shet, V.D., Davis, L.S.: Joint learning for attribute-consistent person re-identification. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8927, pp. 134–146. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-16199-0_10CrossRefGoogle Scholar
  15. 15.
    Kiapour, M.H., Han, X., Lazebnik, S., Berg, A.C., Berg, T.L.: Where to buy it: matching street clothing photos in online shops. In: IEEE International Conference on Computer Vision, pp. 3343–3351 (2015)Google Scholar
  16. 16.
    Kiapour, M.H., Yamaguchi, K., Berg, A.C., Berg, T.L.: Hipster wars: discovering elements of fashion styles. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 472–488. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10590-1_31CrossRefGoogle Scholar
  17. 17.
    Lin, K., Yang, H.F., Liu, K.H., Hsiao, J.H., Chen, C.S.: Rapid clothing retrieval via deep learning of binary codes and hierarchical search. In: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, pp. 499–502. ACM (2015)Google Scholar
  18. 18.
    Lin, Y., Zheng, L., Zheng, Z., Wu, Y., Yang, Y.: Improving person re-identification by attribute and identity learning. arXiv:1703.07220 (2017)
  19. 19.
    Liu, Z., Luo, P., Qiu, S., Wang, X., Tang, X.: DeepFashion: powering robust clothes recognition and retrieval with rich annotations. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1096–1104 (2016)Google Scholar
  20. 20.
    Paszke, A., et al.: PyTorch. http://pytorch.org
  21. 21.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems (2015)Google Scholar
  22. 22.
    Schroff, F., Kalenichenko, D., Philbin, J.: FaceNnet: a unified embedding for face recognition and clustering. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)Google Scholar
  23. 23.
    Shankar, D., Narumanchi, S., Ananya, H., Kompalli, P., Chaudhury, K.: Deep learning based large scale visual recommendation and search for e-commerce. arXiv:1703.02344 (2017)
  24. 24.
    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, pp. 298–307 (2016)Google Scholar
  25. 25.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)
  26. 26.
    Tolias, G., Sicre, R., Jégou, H.: Particular object retrieval with integral max-pooling of CNN activations. arXiv:1511.05879 (2015)
  27. 27.
    Wang, J., et al.: Learning fine-grained image similarity with deep ranking. arXiv:1404.4661 (2014)
  28. 28.
    Wang, X., Sun, Z., Zhang, W., Zhou, Y., Jiang, Y.G.: Matching user photos to online products with robust deep features. In: Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval, pp. 7–14. ACM (2016)Google Scholar
  29. 29.
    Yang, F., et al.: Visual Search at eBay. arXiv:1706.03154 (2017)
  30. 30.
    Yi, D., Lei, Z., Liao, S., Li, S.Z.: Learning face representation from scratch. arXiv:1411.7923 (2014)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Nanyang Technological UniversitySingaporeSingapore

Personalised recommendations