Browse > Article
http://dx.doi.org/10.5909/JBE.2018.23.3.403

CBIR-based Data Augmentation and Its Application to Deep Learning  

Kim, Sesong (Department of Multimedia Engineering, Dongguk University)
Jung, Seung-Won (Department of Multimedia Engineering, Dongguk University)
Publication Information
Journal of Broadcast Engineering / v.23, no.3, 2018 , pp. 403-408 More about this Journal
Abstract
Generally, a large data set is required for learning of deep learning. However, since it is not easy to create large data sets, there are a lot of techniques that make small data sets larger through data expansion such as rotation, flipping, and filtering. However, these simple techniques have limitation on extendibility because they are difficult to escape from the features already possessed. In order to solve this problem, we propose a method to acquire new image data by using existing data. This is done by retrieving and acquiring similar images using existing image data as a query of the content-based image retrieval (CBIR). Finally, we compare the performance of the base model with the model using CBIR.
Keywords
Content-based image retrieval; Data augmentation; Deep learning; Machine learning;
Citations & Related Records
연도 인용수 순위
  • Reference
1 R. Datta, J. Li, and J.Z. Wang, "Content-Based Image Retrieval: Approaches and Trends of the New Age," Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval, ACM, New York, USA, pp.253-262, November, 2005, doi:10.1145/1101826.1101866.   DOI
2 C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, "Going deeper with convolutions," Computer Vision and Pattern Recognition(CVPR), Boston, USA, June, 2015, doi:10.1109/CVPR.2015.7298594.   DOI
3 G. Griffin, A. Holub, and P. Perona. "Caltech-256 object category dataset," California Institute of Technology, 2007.
4 A. Krizhevsky, V. Nair, and G. Hinton, "The CIFAR-10 dataset," 2014, http://www.cs.toronto.edu/kriz/cifar.html.
5 J. Cho, K. lee, E. Shin, G. Choy. S. Do "How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?," 2015. https://arxiv.org/abs/1511.06348.
6 K. Yee, K. Swearingen, K. Li, and M. Hearst, "Faceted metadata for image search and browsing," Proceedings of the SIGCHI conference on Human factors in computing systemsm, Florida, USA, April, 2003, doi:10.1145/642611.642681.   DOI
7 A. Radford, M. Luke, and C. Soumith, "Unsupervised representation learning with deep convolutional generative adversarial networks," International Conference on Learning Representations(ICLR), San Juan, Puerto Rico, May, 2016.