Abstract
With the development of e-commerce in the past years and its growing overlap over the classic way of doing business, many computational and statistical methods were researched and developed to make recommendations for products belonging to the store catalog. Often the data used in recommendation methods involves user interactions, being images and video types of information somewhat unexplored. This work, which we call Xanathar, proposes to extend such paradigm with real-time in-video recommendations for 25 classes of products, using image classifiers and feeding video streams to a modified ResNet-50 network processed on GPU, achieving a top-5 error of 5.17% and running at approximately 60 frames per second. Therefore, describing objects in the scene and proposing related products in-screen, directing user buying experience and creating an immersive and intensive purchase environment.
Supported by Magazine Luiza.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Berman, M., Jégou, H., Vedaldi, A., Kokkinos, I., Douze, M.: Multigrain: a unified image embedding for classes and instances. arXiv preprint. arXiv:1902.05509 (2019)
Chen, Y., et al.: Drop an octave: reducing spatial redundancy in convolutional neural networks with octave convolution. arXiv preprint. arXiv:1904.05049 (2019)
Cohen, W., Ravikumar, P., Fienberg, S.: A comparison of string metrics for matching names and records. In: KDD Workshop on Data Cleaning and Object Consolidation, vol. 3, pp. 73–78 (2003)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 248–255. IEEE (2009)
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The PASCAL visual object classes (VOC) challenge. Int. J. Comput. Vision 88(2), 303–338 (2010)
Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT Press, Cambridge (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint. arXiv:1502.03167 (2015)
Jarrett, K., Kavukcuoglu, K., LeCun, Y., et al.: What is the best multi-stage architecture for object recognition? In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2146–2153. IEEE (2009)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint. arXiv:1412.6980 (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Leach, P., Mealling, M., Salz, R.: A universally unique identifier (UUID) URN namespace. Technical rep. (2005)
Linden, G., Smith, B., York, J.: Amazon. com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)
Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
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, pp. 91–99 (2015)
Scharnowski, F., Hermens, F., Herzog, M.H.: Bloch’s law and the dynamics of feature fusion. Vision Res. 47(18), 2444–2452 (2007)
Shankar, D., Narumanchi, S., Ananya, H., Kompalli, P., Chaudhury, K.: Deep learning based large scale visual recommendation and search for e-commerce. arXiv preprint. arXiv:1703.02344 (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint. arXiv:1409.1556 (2014)
Sivapalan, S., Sadeghian, A., Rahnama, H., Madni, A.M.: Recommender systems in e-commerce. In: World Automation Congress (WAC), pp. 179–184. IEEE (2014)
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-ResNet and the impact of residual connections on learning. In: AAAI, vol. 4, p. 12 (2017)
The HDF Group: Hierarchical Format, version 5 (1997–2018). http://www.hdfgroup.org/HDF5/
Welch, L.: Lower bounds on the maximum cross correlation of signals (corresp.). IEEE Trans. Inf. Theory 20(3), 397–399 (1974)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
de Souza Junior, N.F., da Silva, L.A., Marengoni, M. (2019). Product Recommendation Through Real-Time Object Recognition on Image Classifiers. In: Karray, F., Campilho, A., Yu, A. (eds) Image Analysis and Recognition. ICIAR 2019. Lecture Notes in Computer Science(), vol 11663. Springer, Cham. https://doi.org/10.1007/978-3-030-27272-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-27272-2_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27271-5
Online ISBN: 978-3-030-27272-2
eBook Packages: Computer ScienceComputer Science (R0)