Improving Similarity Search Using Clustered Data
This paper presents a method for improving object search accuracy using a deep learning model. A major limitation to provide accurate similarity with deep learning is the requirement of huge amount of data for training pairwise similarity scores (metrics), which is impractical to collect. Thus, similarity scores are usually trained with a relatively small dataset, which comes from a different domain, causing limited accuracy on measuring similarity. For this reason, this paper proposes a deep learning model that can be trained with a significantly small amount of data, a clustered data which of each cluster contains a set of visually similar images. In order to measure similarity distance with the proposed method, visual features of two images are extracted from intermediate layers of a convolutional neural network with various pooling methods, and the network is trained with pairwise similarity scores which is defined zero for images in identical cluster. The proposed method outperforms the state-of-the-art object similarity scoring techniques on evaluation for finding exact items. The proposed method achieves 86.5% of accuracy compared to the accuracy of the state-of-the-art technique, which is 59.9%. That is, an exact item can be found among four retrieved images with an accuracy of 86.5%, and the rest can possibly be similar products more than the accuracy. Therefore, the proposed method can greatly reduce the amount of training data with an order of magnitude as well as providing a reliable similarity metric.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1317318Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 571
 J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Networks, vol. 61, 2015, pp. 85-117.
 E. Hoffer and N. Ailon, "Deep metric learning using triplet network," International Workshop on Similarity-Based Pattern Recognition. Springer, Cham, 2015.
 K. Lin, H. F. Yang, J. H. Hsiao and C. S. Chen, "Deep learning of binary hash codes for fast image retrieval," 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Boston, MA, 2015, pp. 27-35.
 O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” IJCV, 2015.
 Y. Lecun, L. Bottou, Y. Bengio and P. Haffner, "Gradient-based learning applied to document recognition," in Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov 1998.
 A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in neural information processing systems, 2012.
 C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, "Going deeper with convolutions," 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, 2015, pp. 1-9.
 K. He, X. Zhang, S. Ren and J. Sun, "Deep Residual Learning for Image Recognition," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 2016, pp. 770-778.
 S. Chopra, R. Hadsell, and Y. LeCun, "Learning a similarity metric discriminatively, with application to face verification," 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005, pp. 539-546, vol. 1
 C. Huang, C. C. Loy, and X. Tang, "Local similarity-aware deep feature embedding," Advances in Neural Information Processing Systems. 2016.
 Z. Cao, M. Long, J. Wang, and P. S. Yu, "Hashnet: Deep learning to hash by continuation." arXiv preprint arXiv:1702.00758 (2017).
 J. Huang, R. Feris, Q. Chen, and S. Yan, "Cross-Domain Image Retrieval with a Dual Attribute-Aware Ranking Network," 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, 2015, pp. 1062-1070.
 A. S. Razavian, J. Sullivan, S. Carlsson, and A. Maki, "Visual instance retrieval with deep convolutional networks," ITE Transactions on Media Technology and Applications, 4.3, 2016, pp. 251-258.
 E. Mohedano, K. McGuinness, N. E. O'Connor, A. Salvador, F. Marques, and X. Giro-i-Nieto, "Bags of local convolutional features for scalable instance search." Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval, ACM, 2016.