FACE IMAGE RETRIEVAL SYSTEM USING COMBINATION METHOD OF SELF ORGANIZING MAP AND NORMALIZED CROSS CORRELATION
Keywords:
CBIR, SOM, NCC, Face Image
Abstract
Content based image retrieval (CBIR) is one method in computer vision that is widely applied in various fields of life. In this study, two algorithms will be combined, namely self organizing map (SOM) and normalized cross correlation (NCC) to test the method in the face image retrieval system. The SOM algorithm is used to perform learning on the system created and the NCC method is used to calculate the proximity value between the input image and the image contained in the database to be displayed as the result of image retrieval. The test results in the proposed research show good results with an accuracy rate of face image retrieval of 93.62%. This percentage is higher than using the usual SOM method with an accuracy rate of face image retrieval of 91.62%.
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References
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[2] Singha, M. & Hemachandran, K. (2012). Content Based Image Retrieval using Color and Texture. Signal & Image Processing : An International Journal. Doi: 10.5121/sipij.2012.3104
[3] Shukran, M., Abdullah, M. &Yunus, M. (2021) New Approach on the Techniques of Content-Based Image Retrieval (CBIR) Using Color, Texture and Shape Features. Journal of Materials Science and Chemical Engineering, 9, 51-57. Doi: 10.4236/msce.2021.91005
[4] Latif, A., Rasheed, A., Sajid, U., Jameel, A., Ali, N., Ratyal, NI, Zafar, B., Dar, S., Sajid, M. & Khalil, T. (2019 ). Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review. Mathematical Problems in Engineering. Doi: 10.1155/2019/9658350
[5] Andrejevic, M. & Selwyn, N. (2019). Face recognition technology in schools: critical questions and concerns. Learning, Media and Technology. 45. 1-14. Doi: 10.1080/17439884.2020.1686014
[6] Harish, G., Anusha, Y.A., Anushree D., Diya, M.G. & Jeevika, S. (2020). Strategic Image Retrieval System Using Face Recognition. International Research Journal of Engineering and Technology (IRJET). https://www.irjet.net/archives/V7/i7/IRJET-V7I7955.pdf
[7] Devi, N.S. & Hemachandran, K. (2016). Retrieval and recognition of faces using content-based image retrieval (CBIR) and feature combination method. 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). Doi:10.1109/iccic.2016.7919577
[8] Mishra, S. & Panda, M. (2018). Medical Image Retrieval using Self-Organising Map on Texture Features. Future Computing and Informatics Journal. Doi:10.1016/j.fcij.2018.10.006
[9] Ben-Hdech, A., Ghanou, Y. & El Qadi, A. (2015). A new algorithm for optimizing the self-organizing map. Journal of Theoretical and Applied Information Technology. 78. 415-420. http://www.jatit.org/volumes/Vol78No3/10Vol78No3.pdf
[10] Ahmad, A., Yusoff, R., Ismail, M.N. & Rosli, N.R. (2017). Clustering the imbalanced datasets using modified Kohonen self-organizing map (KSOM). 2017 Computing Conference. Doi:10.1109/sai.2017.8252180
[11] Chandran, S. & Mogiloju, S. (2013). Algorithm for Face Matching Using Normalized Cross-Correlation. International Journal of Engineering and Advanced Technology. 2. 2249-8958. https://www.ijeat.org/
[12] Shambharkar, S.A. & Tirpude, S.C. (2012). A Comparative Study on Retrieved Images by Content Based Image Retrieval System based on Binary Tree, Color, Texture and Canny Edge Detection Approach. International Journal of Advanced Computer Science and Applications(IJACSA), Special Issue on Selected Papers from International Conference & Workshop On Emerging Trends In Technology. http://dx.doi.org/10.14569/SpecialIssue.2012.020106
[13] Iqbal, K., Odetayo, M. & James, A.E. (2012). Content-based image retrieval approach for biometric security using colour, texture and shape features controlled by fuzzy heuristics. Journal of Computer and System Sciences - JCSS. 78. Doi:10.1016/j.jcss.2011.10.013
[14] Bullibabu, R. & Vanitha, V. & Anish, K.S. (2016). Content based Image Retrieval using Color, Texture, Shape and Active Re-Ranking Method. Indian Journal of Science and Technology. 9. Doi: 10.17485/ijst/2016/v9i17/93107
[15] Azemin, M.Z.C., Tamrin, M.I.M., Hilmi, M.R. & Kamal, K.M. (2015). GLCM texture analysis on different color space for pterygium grading. ARPN Journal of Engineering and Applied Sciences. http://www.arpnjournals.com/jeas/research_papers/rp_2015/jeas_0815_2423.pdf
[16] Deswal, M. & Sharma, N. (2012). A Fast HSV Image Color and Texture Detection and Image
Conversion Algorithm. International Journal of Science and Research (IJSR). https://www.ijsr.net/archive/v3i6/MDIwMTQ0ODI=.pdf
[17] Riese, F.M., Keller, S. & Hinz, S. (2020). Supervised and Semi-Supervised Self-Organizing Maps for Regression and Classification Focusing on Hyperspectral Data. Remote Sensing. https://doi.org/10.3390/rs12010007
[18] Asan, U. & Ercan, S. (2012). An Introduction to Self-Organizing Maps. Computational Intelligence Systems in Industrial Engineering: with Recent Theory and Applications (pp.299-319). Doi: 10.2991/978-94-91216-77-0_14
[19] Purbasari, I.Y., Puspaningrum, E.Y. & Putra, A.B.S. (2020). Using Self-Organizing Map (SOM) for Clustering and Visualization of New Students based on Grades. Journal of Physics: Conference Series. Doi: 10.1088/1742-6596/1569/2/022037
[20] Kaso, A. (2018). Computation of the normalized cross-correlation by fast Fourier transform. PLoS ONE 13(9): e0203434. https://doi.org/10.1371/journal.pone.0203434
Published
2021-06-29
How to Cite
Saleh, A., Suryandy, D., & Nainggolan , J. (2021). FACE IMAGE RETRIEVAL SYSTEM USING COMBINATION METHOD OF SELF ORGANIZING MAP AND NORMALIZED CROSS CORRELATION. INFOKUM, 9(2, June), 219-228. Retrieved from https://infor.seaninstitute.org/index.php/infokum/article/view/116
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Articles
