Abstract
There are many styles and details of apparel, including coat length, collar design, sleeve length and other detail properties. The e-commerce platform that manages apparel products needs to quickly and effectively identify the attribute categories of apparel for quick retrieval. Apparel image data contains many detailed features that can be easily deformed and occluded. Traditional image recognition technology has been unable to meet the requirements of its classification accuracy. The neural network based on deep learning can classify the fine-grained attributes of complex objects well after training. In this work, we use the apparel image data to train convolutional neural network for the classification of fine-grained attributes. To improve the classification accuracy, we also integrate the results of different models. The experiments show that the results of multi-model fusion are better than those of single model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Nilsback M.E., Zisserman A.: Automated flower classification over a large number of classes. In: Sixth Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP’08, pp. 722–729 (2008)
Wah C., Branson, S., Welinder, P., Perona, P., Belongie, S: The caltech-ucsd birds-200-2011 dataset, (2011)
Khosla A., Jayadevaprakash N., Yao B., Li, F. F.: Novel dataset for fine-grained image categorization: Stanford dogs. In Proceedings of CVPR Workshop on Fine-Grained Visual Categorization (FGVC), vol. 2, p. 1 (2011)
Krause J., Stark M., Deng J., Li F.F.: 3d object representations for fine-grained categorization. In: IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 554–561 (2013)
Lowe D.G.: Object recognition from local scale-invariant features. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)
Dalal N., Triggs B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893 (2005)
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)
Goodfellow I., Pouget-Abadie J., Mirza M., Xu B., Warde-Farley D., Ozair, S., Bengio, Y.: Generative adversarial nets. In Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Zhang N., Donahue J., Girshick R., Darrell T.: Part-based R-CNNs for fine-grained category detection. In European Conference on Computer Vision, Springer, Cham, pp. 834–849 (2014)
Girshick R., Donahue J., Darrell T., Malik J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
Branson S., Beijbom O., Belongie S.: Efficient large-scale structured learning. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013, pp. 1806–1813 (2013)
Xiao T., Xu Y., Yang K., Zhang J., Peng Y., Zhang Z.: The application of two-level attention models in deep convolutional neural network for fine-grained image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, pp. 842–850 (2015)
Simon M., Rodner E.: Neural activation constellations: unsupervised part model discovery with convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1143–1151 (2015)
Lin T.Y., RoyChowdhury A., Maji S.: Bilinear cnn models for fine-grained visual recognition. In Proceedings of the IEEE International Conference on Computer Vision, pp. 1449–1457 (2015)
FashionAI Global Challenge-Attributes Recognition of Apparel. https://tianchi.aliyun.com/competition/information.htm?spm=5176.100067.5678.2.55ab3a26kPTZlN&raceId=231649 (2018)
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, CVPR 2016, pp. 770–778 (2016)
Huang G., Liu Z., Weinberger K.Q., van der Maaten, L.: Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2261–2269 (2017)
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
He, J., Jia, X., Li, J., Yu, S., Shen, L. (2019). Fine-Grained Apparel Image Recognition Based on Deep Learning. In: Wong, W. (eds) Artificial Intelligence on Fashion and Textiles. AITA 2018. Advances in Intelligent Systems and Computing, vol 849. Springer, Cham. https://doi.org/10.1007/978-3-319-99695-0_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-99695-0_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99694-3
Online ISBN: 978-3-319-99695-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)