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A Neutrosophic Image Retrieval Classifier

by A. A. Salama, Mohamed Eisa, A. E. Fawzy
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 170 - Number 9
Year of Publication: 2017
Authors: A. A. Salama, Mohamed Eisa, A. E. Fawzy
10.5120/ijca2017914798

A. A. Salama, Mohamed Eisa, A. E. Fawzy . A Neutrosophic Image Retrieval Classifier. International Journal of Computer Applications. 170, 9 ( Jul 2017), 1-6. DOI=10.5120/ijca2017914798

@article{ 10.5120/ijca2017914798,
author = { A. A. Salama, Mohamed Eisa, A. E. Fawzy },
title = { A Neutrosophic Image Retrieval Classifier },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2017 },
volume = { 170 },
number = { 9 },
month = { Jul },
year = { 2017 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume170/number9/28095-2017914798/ },
doi = { 10.5120/ijca2017914798 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:17:59.904780+05:30
%A A. A. Salama
%A Mohamed Eisa
%A A. E. Fawzy
%T A Neutrosophic Image Retrieval Classifier
%J International Journal of Computer Applications
%@ 0975-8887
%V 170
%N 9
%P 1-6
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we propose a two-phase Content-Based Retrieval System for images embedded in the Neutrosophic domain. In this first phase, we extract a set of features to represent the content of each image in the training database. In the second phase, a similarity measurement is used to determine the distance between the image under consideration (query image), and each image in the training database, using their feature vectors constructed in the first phase. Hence, the N most similar images are retrieved.

References
  1. Chan Y.K. and Chen C.Y., Image Retrieval System Based on Color Complexity and Color Spatial Features, J. of Systems and Software, 71(1), 65-70, 2004.
  2. Cheng H. D., Guot Y., Zhang Y., A Novel Image Segmentation Approach Based on Neutrosophic Set And Improved Fuzzy C- Means Algorithm, World Scientific Publishing Company, New Math. And Natural Computation, 7(1), 155-171, 2011.
  3. Datta, R., Li, J., Wang, J.Z.: Content-Based Image Retrieval: Approaches and Trends of the New Age. Multimedia Information Retrieval, In Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval ACM, 253–262, 2005.
  4. Eisa M., A New Approach For Enhancing Image Retrieval Using Neutrosophic Set , International Journal of Computer Applications, 95(8), 0975-8887, June 2014.
  5. Hanafy I., Salama A.A. and Mahfouz K., Correlation of Neutrosophic Data , in International Refereed Journal of Engineering and Science (IRJES),1(2), 39-43, 2012.
  6. Hanafy I. M., Salama A. A. and Mahfouz K. M., Neutrosophic Classical Events and Its Probability, International Journal of Mathematics and Computer Applications Research (IJMCAR), 3(1), 171-178, mar 2013.
  7. Hearn D. and Baker M. P., Computer Graphics, Englewood Cliffs, NJ: Prentice Hall, ch. 14, 500-504, 1994.
  8. Ingle D., Bhatia Sh., Content Based Image Retrieval using Combined Features, International Journal of Computer Applications , 44(17), 0975-8887, April 2012.
  9. Ionescu, B., Lambert, P., Coquin, D., Buzuloiu, V.: Color based content retrieval of animation movies: a study. In: Proceedings of the International Workshop on Content- Based Multimedia Indexing (CBMI 2007), Talence, France, 295–302, June 2007.
  10. Kong F. H., Image Retrieval using Both Color And Texture Features, Department of Information science & Technology, Heilongjiang Proceedings of the Eighth International Conference on Machine learning and Cybernetics, Baoding, 4, 2228-2232, 12-15 July 2009.
  11. Kuijk A. A. M., Advanced in Computer Graphics Hardware III, Springer,1991.
  12. Lee I., Muneesawang P., Guan L., Automatic Relevance Feedback for Distributed Content-Based Image Retrieval, ICGT, IEEE.org FLEX Chip signal processor ( MC68175/D), Motorola, 1996.
  13. Lew, M.S., Sebe, N., Djeraba, C., Jain, R.: Content-based multimedia information retrieval: State of the art and challenges. ACM Transactions on Multimedia Computing, Communications, and Applications 2(1), 1–19, 2006.
  14. Ma, W.Y., Zhang, H.J.: Content-Based Image Indexing and Retrieval. In: Handbook of Multimedia Computing, CRC Press, Boca Raton, 227-254, 1999.
  15. Puviarasan N., Bhavani R., Vasanthi A., Image Retrieval Using Combination of Texture and Shape Features, 3(3), 5873-5877, March 2014.
  16. Ramamurthy, B., et al. "CBMIR: Content Based Medical Image Retrieval System Using Texture and Intensity for Dental Images." Eco-friendly Computing and Communication Systems. Springer Berlin Heidelberg,125-134, 2012.
  17. Rui, Y., Huang, T.S., Chang, S.-F.: Image retrieval: current techniques, promising directions, and open issues. Journal of Visual Communication and Image Representation 10(1), 39–62, 1999.
  18. Salama A. A., Abdelfattah M., Eisa M., Distances, Hesitancy Degree and Flexible Querying via Neutrosophic Sets, International of Computer Applications, 101(10), 0975-8887, September 2014.
  19. Salama A. A., Neutrosophic Crisp Point &Neutrosophic Crisp Ideals, Neutrosophic Sets and Systems, 1(1),50-54,2013.
  20. Salama A. A. and Smarandache F., Filters Via Neutrosophic Crisp Sets, Neutrosophic Sets and Systems, 1(1), 34-38, 2013.
  21. Salama A. A. and Elagamy H., NeutrosophicFillters, International Journal of Compter Science Engineering and Internation Technology Research (IJCSEITR), 3(1), 307-312, 2013.
  22. Salama A. A., The Concept of Neutrosphic Set and Basic Properties of Neutrosophic Set Operations, WASET 2012 PARIS, FRANC., International university of Science, Engineering and technology, 2012.
  23. Shannon C. E., A Mathematical Theory of Communication, The Bell System Technical Journal, 27, 379-423, 623-656, Jully, October, 1948.
  24. Sinha M. N., Udai A. D., Computer Graphics, Taha McGraw – Hill publishing company limited.
  25. Smarandache F., Neutrosophy and Neutrosophic Logic, First International Conference on Neutrosophy, Neutrosophic logic, Set, Probability, and Statistics, University of New Mexico, Gallup, NM 87301, USA, 2002.
  26. Smarandache F., A Unifying Field in Logics: Neutrosophic Logic. Neutrosophy, Neutrosophic Set, Neutrosophic Probability. Ameican Research Press, Rehoboth, NM, 1-41, 1999.
  27. Smarandache F., Neutrosophic Set, A Generalization of The Intuitionistic Fuzzy Sets, Inter. J. Pure Appl. Math., 24, 287-297, 2005.
  28. Smarandache F., Introduction to Neutrosophic Measure, Neutrosophic Integral and Neutrosophic Probability 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Images in the Neutrosophic Domain similarity measures Euclidean distances.

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