Abstract
It is prevalent to perform hashing on the basis of the well-known Principal Component Analysis (PCA), e.g., [1–4]. Of all those PCA-based methods, Iterative Quantization (ITQ) [1] is probably the most popular one due to its superior performance in terms of retrieval accuracy. However, the optimization problem in ITQ is severely under-deterministic, thereby the quality of the produced hash codes may be depressed. In this paper, we propose a new hashing method, termed Isotropic Iterative Quantization (IITQ), that extends the formulation of ITQ by incorporating properly the isotropic prior proposed by [3]. The optimization problem in IITQ is complicate, non-convex in nature and therefore not easy to solve. We devise a proximal method that can solve problem in a practical fashion. Extensive experiments on two benchmark datasets, CIFAR-10 [5] and 22K-LabelMe [6], show the superiorities of our IITQ over several existing methods.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Gong, Y., Lazebnik, S., Gordo, A., Perronnin, F.: Iterative quantization: a procrustean approach to learning binary codes for large-scale image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2916–2929 (2013)
Gong, Y., Lazebnik, S.: Iterative quantization: a procrustean approach to learning binary codes. In: CVPR, pp. 817–824 (2011)
Kong, W., Li, W.: Isotropic hashing. In: NIPS, pp. 1655–1663 (2012)
Xia, Y., He, K., Kohli, P., Sun, J.: Sparse projections for high-dimensional binary codes. In: CVPR, pp. 3332–3339 (2015)
Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images (2009)
Torralba, A., Fergus, R., Weiss, Y.: Small codes and large image databases for recognition. In: CVPR (2008)
Weiss, Y., Fergus, R., Torralba, A.: Multidimensional spectral hashing. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 340–353. Springer, Heidelberg (2012)
Kulis, B., Darrell, T.: Learning to hash with binary reconstructive embeddings. In: NIPS, pp. 1042–1050 (2009)
Kulis, B., Jain, P., Grauman, K.: Fast similarity search for learned metrics. IEEE Trans. Pattern Anal. Mach. Intell. 31(12), 2143–2157 (2009)
Mu, Y., Yan, S.: Non-metric locality-sensitive hashing. In: AAAI (2010)
Sánchez, J., Perronnin, F.: High-dimensional signature compression for large-scale image classification. In: CVPR, pp. 1665–1672 (2011)
Yu, F.X., Kumar, S., Gong, Y., Chang, S.F.: Circulant binary embedding(2014)
Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: NIPS, pp. 1753–1760 (2008)
Xu, H., Wang, J., Li, Z., Zeng, G., Li, S., Yu, N.: Complementary hashing for approximate nearest neighbor search. In: ICCV, pp. 1631–1638 (2011)
Wang, J., Kumar, S., Chang, S.-F.: Semi-supervised hashing for large-scale search. IEEE Trans. Pattern Anal. Mach. Intell. 34(12), 2393–2406 (2012)
Mu, Y., Shen, J., Yan, S.: Weakly-supervised hashing in kernel space. In: CVPR, pp. 3344–3351 (2010)
Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: VLDB, pp. 518–529 (1999)
Andoni, A., Indyk, P.: Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Commun. ACM 51(1), 117–122 (2008)
Raginsky, M., Lazebnik, S.: Locality-sensitive binary codes from shift-invariant kernels. In: NIPS, pp. 1509–1517 (2009)
Datar, M., Immorlica, N., Indyk, P., Mirrokni, V.S.: Locality-sensitive hashing scheme based on p-stable distributions. In: ACM Symposium on Computational Geometry, pp. 253–262 (2004)
Kulis, B., Grauman, K.: Kernelized locality-sensitive hashing for scalable image search. In: ICCV, pp. 2130–2137 (2009)
Attouch, H., Bolte, J.: On the convergence of the proximal algorithm for nonsmooth functions involving analytic features. Math. Program. 116(1–2), 5–16 (2009)
Broder, A.Z., Charikar, M., Frieze, A.M., Mitzenmacher, M.: Min-wise independent permutations. J. Comput. Syst. Sci. 60, 327–336 (1998)
Ping Li and Arnd Christian König: Theory and applications of b-bit minwise hashing. Commun. ACM 54(8), 101–109 (2011)
Chu, M.T.: Constructing a hermitian matrix from its diagonal entries and eigenvalues. SIAM J. Matrix Anal. Appl 16, 207–217 (1995)
Gower, J.C., Dijksterhuis, G.B.: Procrustes Problems. Oxford, Oxford University Press (2004)
Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42(3), 145–175 (2001)
Qiao, L., Chen, S., Tan, X.: Sparsity preserving projections with applications to face recognition. Pattern Recogn. 43(1), 331–341 (2010)
Acknowledgement
This work is supported by NSFC 61502238, NSFC 61532009, BK2012045 and 15KJA520001.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Li, L., Liu, G., Liu, Q. (2016). Advancing Iterative Quantization Hashing Using Isotropic Prior. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9517. Springer, Cham. https://doi.org/10.1007/978-3-319-27674-8_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-27674-8_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27673-1
Online ISBN: 978-3-319-27674-8
eBook Packages: Computer ScienceComputer Science (R0)