Abstract
Traditional clustering methods are inherently difficult to handle with a large scale of images, since it is expensive to store all the data and to make pairwise comparison of high-dimensional vectors. To solve this problem, we propose a novel Binary K-means for accurate image clustering. After hashing the data into binary codes, the weights assigned to the binary data are based on the global information and the weights for the binary centers are adapted iteratively. Then, in each iteration, with the center-adaptive weights the distance between the binary data and the binary centers is computed by the weighted Hamming distance. As the data and centers are presented in binary, we can build a hash table to speed up the comparison. We evaluate the proposed method on three large datasets and the experiments show that, the proposed method can achieve a good clustering performance with small storage and efficient computation.
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
Notes
- 1.
For binary codes, we can represent them as 0/1 or −1/1 interchangeably since we use Hamming distance.
References
Bilen, H., Pedersoli, M., Tuytelaars, T.: Weakly supervised object detection with convex clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1081–1089 (2015)
Gong, Y., Lazebnik, S.: Iterative quantization: a procrustean approach to learning binary codes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 817–824 (2011)
Gong, Y., Pawlowski, M., Yang, F., Brandy, L., Bourdev, L., Fergus, R.: Web scale photo hash clustering on a single machine. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 19–27 (2015)
Gordo, A., Perronnin, F., Gong, Y., Lazebnik, S.: Asymmetric distances for binary embeddings. IEEE Trans. Pattern Anal. Mach. Intell. 36(1), 33–47 (2014)
Heo, J.P., Lee, Y., He, J., Chang, S.F., Yoon, S.E.: Spherical hashing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2957–2964 (2012)
Jegou, H., Douze, M., Schmid, C.: Product quantization for nearest neighbor search. IEEE Trans. Pattern Anal. Mach. Intell. 33(1), 117–128 (2011)
Kim, G., Sigal, L., Xing, E.P.: Joint summarization of large-scale collections of web images and videos for storyline reconstruction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4225–4232 (2014)
Kong, W., Li, W.J., Guo, M.: Manhattan hashing for large-scale image retrieval. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 45–54 (2012)
Lcun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Lee, Y.J., Grauman, K.: Shape discovery from unlabeled image collections. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2254–2261 (2009)
Mahmood, A., Mian, A., Owens, R.: Semi-supervised spectral clustering for image set classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 121–128 (2014)
Namor, A.F.D.D., Shehab, M., Khalife, R., Abbas, I.: The German traffic sign recognition benchmark: a multi-class classification competition. In: The 2011 International Joint Conference on Neural Networks (IJCNN), pp. 1453–1460 (2011)
Norouzi, M., Punjani, A., Fleet, D.J.: Fast exact search in hamming space with multi-index hashing. IEEE Trans. Pattern Anal. Mach. Intell. 36(6), 1107–1119 (2013)
Sivic, J., Russell, B.C., Zisserman, A., Freeman, W.T., Efros, A.A.: Unsupervised discovery of visual object class hierarchies. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Torralba, A., Fergus, R., Weiss, Y.: Small codes and large image databases for recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Wang, J., Liu, W., Kumar, S., Chang, S.: Learning to hash for indexing big data - a survey. Proc. IEEE 104(1), 34–57 (2015)
Weber, M., Welling, M., Perona, P.: Towards automatic discovery of object categories. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 101–108 (2010)
Acknowledgments
This work is supported by the Shenzhen Municipal Development and Reform Commission (Disciplinary Development Program for Data Science and Intelligent Computing), and the Shenzhen Engineering Laboratory of Broadband Wireless Network Security.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Lan, Y., Weng, Z., Zhu, Y. (2018). Center-Adaptive Weighted Binary K-means for Image Clustering. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10736. Springer, Cham. https://doi.org/10.1007/978-3-319-77383-4_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-77383-4_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77382-7
Online ISBN: 978-3-319-77383-4
eBook Packages: Computer ScienceComputer Science (R0)