Int J Performability Eng ›› 2018, Vol. 14 ›› Issue (11): 2601-2611.doi: 10.23940/ijpe.18.11.p6.26012611

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Locality Preserving Hashing based on Random Rotation and Offsets of PCA in Image Retrieval

Shan Zhao and Yongsi Li*   

  1. School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454000, China
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  • About author:Shan Zhao graduated from Xidian University with a Ph.D. She entered the School of Computer Science and Technology of Henan Polytechnic University in 2007 and became an associate professor in 2010. She is currently a visiting scholar at the University of Limerick in Ireland. She is also a member of the China Computer Federation. Her current research interests include image processing and pattern recognition.Yongsi Li is a Master's student in the School of Computer Science and Technology at Henan Polytechnic University. Her research interests include image retrieval based on hash and deep learning.

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.

Key words: manifold, data reduction, hashing, PCA, LPP, random rotation