# Locality Preserving Hashing based on Random Rotation and Offsets of PCA in Image Retrieval
##### Volume 14, Number 11, November 2018, pp. 2601-2611 DOI: 10.23940/ijpe.18.11.p6.26012611
## Shan Zhao and Yongsi Li^{}
School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454000, China
(Submitted on August 20, 2018; Revised on September 15, 2018; Accepted on October 17, 2018)
## Abstract:
Manifold-based subspace feature extraction methods have recently been deeply studied in data dimensionality reduction. Inspired by PCA Hashing (PCAH), if the Locality Preserving Projection (LPP) is directly used in the hash image retrieval, it is prone to shortcomings such as being inefficient and time-consuming. In order to address these deficiencies, this paper mainly combines Principal Component Analysis (PCA) and manifold subspace feature extraction method LPP, and we present a RLPH framework using random rotation. Among them, PCA processing solves the eigenvalue problem encountered in the calculation of LPP, thereby improving the recognition effect of the algorithm. The PCA projection needs to ensure that the variance of the sample points after projection is as large as possible. However, projections of small variance may produce unnecessary redundancy and noise. Therefore, in the subspace after the PCA projection, we only extract the eigenvectors that contain most of the information at the top of the PCA projections. Then, we utilize a random orthogonal matrix to randomly rotate and shifts the eigenvectors and the reduced-dimensional sample obtained after the top eigenvectors of the PCA projection is subjected to LPP mapping. Random rotation produces many thin projection matrices blocks that are then concatenated into one final projection matrix. Random rotation is a key step in this paper that minimizes the quantization error for codes. The proposed method greatly improves the retrieval efficiency, and extensive experiments demonstrate its effectiveness.
**References: 30**
- R. Xia, Y. Pan, H. Lai, C. Liu, and S. Yan, “Supervised Hashing for Image Retrieval via Image Representation Learning,” in
*Proceedings of **AAAI Conference on Artificial Intelligence*, 2012
- T. Dean, M. A. Ruzon, M. Segal, J. Shlens, S. Vijayanarasimhan, and J. Yagnik, “Fast, Accurate Detection of 100,000 Object Classes on A Single Machine,” in
*Proceedings of** **IEEE Conference on Computer Vision and Pattern Recognition*, pp. 1814-1821, 2013
- C. Strecha, A. M. Bronstein, M. M. Bronstein, and P. Fua, “LDAHash: Improved Matching with Smaller Descriptors,”
*IEEE Transactions on Pattern Analysis & Machine Intelligence*, Vol. 34, No. 1, pp. 66-78, 2012
- M. Charikar, “Similarity Estimation Techniques from Rounding Algorithm,” in
*Proceedings of* *ACM Symposium on Theory of Computing*, pp. 380-388, 2002
- Y. Liu, P. Yan, and R. K. Xia, et al, “FP-CNNH: A Fast Image Hashing Algorithm Based on Deep Convolutional Neural Network,”
*Computer Science,* Vol. 43, No. 9, pp. 39-51,2016
- J. Heo, Y. Lee, J. He, S. Chang, and S. Yoon, “Spherical Hashing,” in
*Proceedings of** IEEE Conference on Computer Vision and Pattern Recognition*, pp. 2957-2964, 2012
- B. Kulis and K. Grauman, “Kernelized Locality-Sensitive Hashing for Scalable Image Search,” in
*Proceedings of IEEE Conference on Computer Vision and Pattern Recognition*, pp. 2130-2137, 2009
- M. Norouzi and D. J. Fleet, “Minimal Loss Hashing for Compact Binary Codes,” in
*Proceedings of* *International Conference on International Conference on Machine Learning** **Omnipress*, pp. 353-360, 2011
- A. Rahimi and B. Recht, “Random Features for Large-Scale Kernel Machines,” in
*Proceedings of* *International Conference on Neural Information Processing Systems*, pp. 1177-1184,* *2007
- M. Raginsky and S. Lazebnik, “Locality Sensitive Binary Codes from Sift-Invariant Kernels,”
*Advances in Neural Information Processing Systems*, 2009
- J. Wang, S. Kumar, and S. F. Chang, “Semi-Supervised Hashing for Scalable Image Retrieval,” in
*Proceedings of IEEE Conference on Computer Vision and Pattern Recognition*, pp. 3424-3431, 2010
- Y. Weiss, A. Torralba, and R. Fergus, “Spectral Hashing,” in
*Advances in Neural Information Processing Systems*, 2008
- S. Arya, D. M. Mount, N. S. Netanyahu, R. Silverman, and A. Y. Wu, “An Optimal Algorithm for Approximate Nearest Neighbor Searching,” in
*Proceedings of the Fifth Annual ACM-SIAM Symposium on Discrete Algorithms*, pp. 573-582, 1994
- J. H. Friedman, J. L. Bentley, and R. A. Finkel, “An Algorithm for Finding Best Matches in Logarithmic Expected Time,”
*ACM*, Vol. 3, No. 3, pp. 209-226, 1977
- N. Kumar, L. Zhang, and S. Nayar, “What is A Good Nearest Neighbors Algorithm for Finding Similar Paths in Images?” in
*Proceedings of the 10th European Conference on Computer Vision*, pp. 364-378, 2008
- M. Muja and D. G. Lowe, “Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration,” in
*Proceedings of* *VISAPP International Conference on Computer Vision Theory and Applications*, pp. 331-340, 2009
- C. Silpa-Anan and R. Hartley, “Optimised KD-Trees for Fast Image Descriptor Matching,” in
*Proceedings of IEEE Conference on Computer Vision and Pattern Recognition*, pp. 1-8, 2008
- K. He, F. Wen, and J. Sun, “K-Means Hashing: An Affinity-Preserving Quantization Method for Learning Binary Compact Codes,” in
*Proceedings of** IEEE Conference on Computer Vision and Pattern Recognition*, 2013
- W. Kong and J. W. Li, “Isotropic Hashing,” in
*Advances in Neural Information Processing Systems*, 2012
- C. Leng, J. Cheng, and H. Lu, “Random Subspace for Binary Codes Learning in Large Scale Image Retrieval,” in
*Proceedings of ACM SIGIR Conference*, 2014
- W. Liu, J. Wang, S. Kumar, and S. F. Chang, “Hashing with Graphs,” in
*Proceedings of* *International Conference on Machine Learning *(*ICML*), pp. 1-8, Bellevue, Washington, USA, 2011
- D. Zhang, J. Wang, D. Cai, and J. Lu, “Self-Taught Hashing for Fast Similarity Search,” in
*Proceedings of International ACM SIGIR Conference*, 2010
- A. Shrivastava and P. Li, “In Defense of MinHash over SimHash,” in
*Proceedings of International Conference on Artificial Intelligence and Statistics*, 2014
- H. Abdi and L. J. Williams, “Principal Component Analysis,”
*Wiley Interdisciplinary Reviews Computational Statistics*, Vol. 2, No. 4, pp. 433-459, 2010
- X. He and P. Niyogi, “Locality Preserving Projections,”
*Advances in Neural Information **Processing Systems*, Vol. 16, No. 1, pp. 186-197, 2003
- C. Leng, J. Cheng, T. Yuan, X. Bai, and H. Lu, “Learning Binary Codes with Bagging PCA,” in
*Proceedings of European Conference on Machine Learning*, pp. 177-192, 2014
- P. Li and P. Ren, “R
^{2}PCAH: Hashing with Two-Fold Randomness on Principal Projections,” *Neurocomputing*, pp. 236-244, 2017
- J. Wang, S. Kumar, and S. F. Chang, “Sequential Projection Learning for Hashing with Compact Codes,” in
*Proceedings of International Conference on Machine Learning*, pp. 1127-1134, 2010
- B. Xu, J. Bu, Y. Lin, C. Chen, X. He, and D. Cai, “Harmonious Hashing,” in
*Proceedings of International Joint Conference on Artificial Intelligence*, 2013
- G. Irie, Z. Li, X. M. Wu, and S. F. Chang, “Locally Linear Hashing for Extracting Non-Linear Manifolds,” in
*Proceedings of** IEEE Conference on Computer Vision and Pattern Recognition & IEEE Computer Society*, pp. 2123-2130, 2014
Please note : You will need Adobe Acrobat viewer to view the full articles. |