THE METHOD OF FINDING THE SPAM IMAGES BASED ON THE HASH OF THE KEY POINTS OF THE IMAGE

Authors

  • Nikolay I. Korsunov
  • Dmitri A. Toropchin

DOI:

https://doi.org/10.47839/ijc.15.4.857

Keywords:

perceptual hash, image descriptor, image recognition, image search.

Abstract

The article deals with the problems of the existing image recognition algorithms and also presents a method allowing to minimize the drawbacks of existing algorithms, and even to surpass them in certain moments. The proposed method is based on the description of the key points of the image by using perceptual hash. As a demonstration of application of the developed algorithm it is proposed to make the spam filter for images. The purpose of the spam filter is to separate, to divide the images from the collection into certain classes. The core of the spam filter is the developed method. In its conclusion the article contains information that shows the accuracy and performance of the proposed method in comparison with existing methods.

References

Computer vision. The modern approach, Moscow, Williams Publishing House, 2004. – 928 p. (in Russian).

L. Shapiro, G. Stockman, Computer Vision, Moscow, Bean, Laboratory Knowledge, 2006, 752 p. (in Russian).

U. V. Vizilter, S. U. Zheltov, A.V. Bondarenko, M. V. Ososkov, A. V. Morzhin, Image Processing and Analysis Tasks in Machine Vision: Lectures and Practical Classes, Moscow, Fizmatkniga, 2010, 672 p. (in Russian).

V. I. Vasiliev, The Problem of Learning Pattern Recognition. The Principles, Algorithms, Implementation, Kiev, Vyshchaya School, 1989, 64 p. (in Russian).

D. V. Sorokin, A. S. Krylov, “A projection local image descriptor,” Pattern Recognition and Image Analysis, Springer, Vol. 22, No. 1, pp. 380-385, 2012.

Simple and DCT perceptual hash-algorithms [Online] www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html

Zeng Jie, “A novel block-DCT and PCA based image perceptual hashing algorithm,” International Journal of Computer Science Issues, Vol. 10, Issue 1, No 3, January 2013.

F. X. Standaert, F. Lefebvre, G. Rouvroy, B. M. Macq, J. J. Quisquater, J. D. Legat, “Practical evaluation of a radial soft hash algorithm,” in Proceedings of the IEEE International Symposium on Information Technology, Coding and Computing (ITCC), April 2005, Vol. 2, pp. 89-94.

D. Marrand, E. Hildret, Theory of Edge Detection, 1979, pp. 187-215.

A. S. Krylov, D. V. Sorokin, D. V. Yurin, E. V. Semeikina, “Use of color information for keypoints detection and descriptors construction,” Lecture Notes in Computer Science, Intelligent Science and Intelligent Data Engineering, Springer, Vol. 7202, pp. 389−396, 2012.

J. Russel, R. Cohn, Difference of Gaussians, 2012, 76 p.

M. Egmont-Petersen, D. de Ridder, H. Handels, “Image processing with neural networks – a review,” Pattern Recognition, Vol. 35, Issue 10, pp. 2279-2301, 2002.

R. V. Hemming. Numerical Methods for Scientists and Engineers, Science, 1972, 400 p.

https://en.wikipedia.org/wiki/Spamming

G. Bradski and A. Kaehler, Learning open CV, First Edition, O’Reilly Media, Inc., September 2008.

Open Computer Vision Library [Online] http://opencv.org/

www.flickr.com

Encyclopedia of Biometrics, S. Z. Li, A. K. Jain (eds.), Springer, 2009, 1445 p.

C. Zauner, Implementation and Benchmarking of Perceptual Image Hash Functions, PhD Thesis, 2010.

N. I. Korsunov, D. A. Toropchin, “Recognition method of near-duplicate images based on the perceptual hash and image key points using,” in Proceedings of the 2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications (IDAACS’2015), Warsaw, Poland, 24-26 September 2015, Vol. 1, pp. 261-265.

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Published

2016-12-29

How to Cite

Korsunov, N. I., & Toropchin, D. A. (2016). THE METHOD OF FINDING THE SPAM IMAGES BASED ON THE HASH OF THE KEY POINTS OF THE IMAGE. International Journal of Computing, 15(4), 259-264. https://doi.org/10.47839/ijc.15.4.857

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Articles