Abstract
We investigate the problem of a visual similarity-based recommender system, where cosmetic products are recommended based on the preferences of people who share similarity of visual features. In this work we train a Siamese convolutional neural network, using our own dataset of cropped eye regions from images of 91 female subjects, such that it learns to output feature vectors that place images of the same subject close together in high-dimensional space. We evaluate the trained network based on its ability to correctly identify existing subjects from unseen images, and then assess its capability to find visually similar matches amongst the existing subjects when an image of a new subject is input.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Resnick, P., Varian, H.: Recommender systems. Commun. ACM, 56–58 (1997)
Gomez-Uribe, C.A., Hunt, N.: The Netflix recommender system: algorithms, business value, and innovation. ACM Trans. Manag. Inf. Syst. 6(4), 13 (2016)
Smith, B., Linden, G.: Two decades of recommender systems at Amazon.com. IEEE Internet Comput. 21(3), 12–18 (2017)
Bhatti, N., et al.: Mobile cosmetics advisor: an imaging based mobile service. In: SPIE 7542 Multimedia on Mobile Devices (2010)
McAuley, J., Targett, C., Shi, Q., Van Den Hengel, A.: Image-based recommendations on styles and substitutes. In: International ACM SIGIR Conference on Research and Development in Information Retrieval (2015)
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)
Wang, J., et al.: Learning fine-grained image similarity with deep ranking. In: IEEE Conference on Computer Vision and Pattern Recognition (2014)
Chopra, S., Hadsell, R., Lecun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: IEEE Conference on Computer Vision and Pattern Recognition (2005)
Wang, X., Gupta, A.: Unsupervised learning of visual representations using videos. In: IEEE International Conference on Computer Vision (2015)
Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In ICML Deep Learning Workshop (2015)
Yu, Q., et al.: Sketch me that shoe. In: IEEE Conference on Computer Vision and Pattern Recognition (2016)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886–893 (2005)
Cortes, C., Vapnik, V.: Support vector networks. Mach. Learn. 20(3), 273–297 (1995)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)
Chopra, S., Hadsell, R., LeCunn, Y.: Dimensionality reduction by learning an invariant mapping. In: Computer Vision and Pattern Recognition, pp. 539–546 (2005)
Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations (2014)
van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Holder, C.J., Obara, B., Ricketts, S. (2019). Visual Siamese Clustering for Cosmetic Product Recommendation. In: Carneiro, G., You, S. (eds) Computer Vision – ACCV 2018 Workshops. ACCV 2018. Lecture Notes in Computer Science(), vol 11367. Springer, Cham. https://doi.org/10.1007/978-3-030-21074-8_40
Download citation
DOI: https://doi.org/10.1007/978-3-030-21074-8_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-21073-1
Online ISBN: 978-3-030-21074-8
eBook Packages: Computer ScienceComputer Science (R0)