一种结合正交分解及BoVW的图像安全检索方法

doi:10.3969/j.issn.0255-8297.2018.02.009

Abstract

Abstract:

We propose an image security retrieval method combining orthogonal decomposition and bag of visual word model (BoVW) in this paper to solve the problems that existing encryption algorithms for the current encryption domain image retrieval cannot meet the needs of different applications, and the algorithms relying on underlying image generally suffer lower retrieval accuracy. By introducing orthogonal decomposition framework, the image data domain is divided into the encryption domain and the retrieval domain. The encryption operation and feature extraction operation are independent, without interaction between them. In the encryption domain, users can choose any encryption method as needed. In the retrieval domain, the visual word bag model framework is introduced, and the image is represented as the visual word histogram, which reduces the semantic gap between the underlying feature and the high-level semantics, accordingly, improving the retrieval precision. Experimental results show that the proposed method provides higher security and higher retrieval precision than the current encryption domain image retrieval techniques.

Key words: cloud environment, orthogonal decomposition, image security retrieval, bag of visual words

CLC Number:

TN391

ZHAO Xiao, XU Yan-yan, GONG Jia-ying, SONG Fang-zhen. A Secure Image Retrieval Method Based on Combined Orthogonal Decomposition and BoVW[J]. Journal of Applied Sciences, 2018, 36(2): 299-308.

References

[1] Shashank J, Kowshik P, Srinathan K. Private content based image retrieval[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2008:1-8.
[2] Erkin Z, Franz M, Guajardo J. Privacy-preserving face recognition[J]. Privacy Enhancing Technologies, 2009, 5672:235-253.
[3] Lu W, Varna A L, Swaminathan A. Secure image retrieval through feature protection[C]//IEEE International Conference on Acoustics, Speech and Signal Processing. ICASSP, 2009:1533-1536.
[4] Hsu C Y, Lu C S, Pei S C. Image feature extraction in encrypted domain with privacypreserving SIFT[J]. IEEE Transactions on Image Process, 2012, 21(11):4593-607.
[5] Zhao R, Grosky W I. Narrowing the semantic gap-improved text-based web document retrieval using visual features[J]. IEEE Transactions on Multimedia, 2002, 4(2):189-200.
[6] Sivic J, Zisserman A. Video Google:A text retrieval approach to object matching in videos[C]//IEEE International Conference on Computer Vision. IEEE Computer Society, 2003, 2:1470.
[7] Jiang L, Xu Z, Xu Y. Commutative encryption and watermarking based on orthogonal decomposition[J]. Multimedia Tools & Applications, 2014, 70(3):1617-1635.
[8] Lowe D G. Distinctive image features from scale-invariant key-points[J]. International Journal of Computer Vision, 2004, 60(2):91-110.
[9] Li F F, Fergus R, Perona P. Learning generative visual models from few training examples:an incremental Bayesian approach tested on 101 object categories[C]//Conference on Computer Vision and Pattern Recognition Workshop. IEEE Computer Society, 2004:178.
[10] Jones K S. A statistical interpretation of term specifcity and its application in retrieval[J]. Journal of Documentation, 1972, 60(1):493-502.
[11] Filip I, SzeiderT I. Givens orthogonal transformation-based estimator versus RLS estimatorcase study for an induction generator model[M]//Soft Computing Applications.[S.l.]:Springer International Publishing, 2016:1287-1299.
[12] Lin G H, Wang T, Wang Y Y. Orthogonal transformation state estimation based on generalized tellegen's theorem[C]//Advanced Materials Research, 2014, 960:1034-1038.
[13] Lowe D G. Object recognition from local scale-invariant features[C]//The Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999:1150-1157.
[14] Lindeberg T, Lindeberg T. Scale-space theory:a basic tool for analyzing structures at different scales[J]. Journal of Applied Statistics, 1994, 21(2):224-270.
[15] Lindeberg T. Detecting salient blob-like image structures and their scales with a scale-space primal sketch:a method for focus-of-attention[J]. International Journal of Computer Vision, 1993, 11(3):283-318.
[16] Mikolajczyk K, Schmid C. An afne invariant interest point detector[C]//European Conference on Computer Vision.[S.l.]:Springer-Verlag, 2002:128-142.
[17] Macqueen J. Some methods for classifcation and analysis of multivariate observations[C]//Proceedings of Berkeley Symposium on Mathematical Statistics & Probability. 1966:281-297.
[18] Wang J Z, Li J, Wiederhold G. Simplicity:semantics-sensitive integrated matching for picture libraries[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2001, 23(9):947-963.
[19] Lu W, Varna A L, Wu M. Confdentiality-preserving image search:a comparative study between homomorphic encryption and distance-preserving randomization[J]. IEEE Access, 2014, 2:125-141.

A Secure Image Retrieval Method Based on Combined Orthogonal Decomposition and BoVW

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 1

Recommended Articles

Metrics

Comments