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Vehicle Detection Using Fuzzy C-Means Clustering Algorithm

Yıl 2020, , 85 - 91, 01.10.2020
https://doi.org/10.18100/ijamec.799431

Öz

Vehicle detection and identification are very important functions in the field of traffic control and management. Generally, a study should be conducted on big data sets and area characteristics to get closer to this function. The aim is to find the most appropriate model for these data. Also, the model that is prepared for the data aims to recognize the factors on the image. In other words, it aims to assign factors to the right classes and differentiate them. A classification of the image is made in that way. In this study, a vehicle identification system, in which Fuzzy C-Means Algorithm is used for image segmentation and the Support Vector Machine is used for image classification, is presented. The currentness of these methods is their most important property. The obtained results show that the selected methods are applied successfully and effectively.

Kaynakça

  • T. Sufi,”A Case Study on Market Segmentation, Positioning and Classification of Multi-Brand Hotel Chains”, Emerging Dynamics of Indian Tourism and Hospitality, 2018, 87-97.
  • O. Ozdemir, and A. Kaya,”Effect of parameter selection on fuzzy clustering”. Mehmet Akif Ersoy University Applied Scince Journal, vol. 2, pp. 22-33, 2018.
  • M. M. Chi, Q. Qian Q., and J. A. Benediktsson “Cluster-based ensemble classification for hyperspectral remote sensing”, in Proc. IEEE IGARSS, 2018, 209–212.
  • P. Kamavisdar, et al., “A Survey on Image Classification Approaches and Techniques”, International Journal of Advanced Research in Computer and Communication Engineering, vol. 2, Issue 1, 2008.
  • J. C. Bezdek, “Pattern recognition with fuzzy objective function algorithms: Plenum”, New York, 1982, pp. 256.
  • D. L. Pham, J. L. Prince, “Adaptive Fuzzy Segmentation of Magnetic Resonance Images”, IEEE Trans. Medical Imaging, vol. 18, pp. 737–752, 1999.
  • N. Dhanachandra, K. Manglem and Y. J. Chanu, “Image segmentation using k-means clustering algorithm and subtractive clustering algorithm”, Procedia Computer Science, vol.54, pp.764-771, 2015.
  • C. Ambroise, G. Govaert, “Convergence of an EM-type algorithm for spatial clustering”, Pattern Recognition Letters vol. 19, pp. 919–927, 1998.
  • T McInerney and D. Terzopoulos, “Deformable models in medical image analysis: A survey”, Medical Image Analysis, vol. 1, no. 2, pp. 91-108, 1996.
  • D. Q. Zhang. and S. C. Chen, “Clustering in completed data using Kernelbased fuzzy cmeans algorthim”, Neural Processing Letters, vol.18, no. 3, pp. 155-162, 2003.
  • F. Meyer and S. Beucher, “Morphology segmentation”, Journal Visual Communications and Image Representation, vol. 1, no. 1, pp. 21-26, 1990.
  • H. Shamsi, et al, “A modified fuzzy c means clustering with spatial information for image segmentation” International journal of computer theory and engineering, IACSIT, 2012.
  • A. Khwairakpam, D. Kandar, D., B. Paul, “Noise reduction in synthetic aperture radar images using fuzzy logic and genetic algorithm. Microsyst Technol vol. 25,pp. 1743–1752, 2019.
  • J. Zhang and Z. Ma, “Hybrid Fuzzy Clustering Method Based on FCM and Enhanced Logarithmical PSO” Computational Intelligence and Neuroscience, vol.2020, 2020.
  • R. Duda, and P. Hart, “Pattern classification and scene analysis”, Wiley-Interscience, New York, 1973.
  • A. Seker A. and A. G. Yuksek, “Stacked Autoencoder Method for Fabric Defect Detection” Cumhuriyet Science Journal., vol. 38, no. 2, 2017.
  • V. N. Vapnik, “The Nature of Statistical Learning Theory” Springer-Verlag, New York, 1995.
Yıl 2020, , 85 - 91, 01.10.2020
https://doi.org/10.18100/ijamec.799431

Öz

Kaynakça

  • T. Sufi,”A Case Study on Market Segmentation, Positioning and Classification of Multi-Brand Hotel Chains”, Emerging Dynamics of Indian Tourism and Hospitality, 2018, 87-97.
  • O. Ozdemir, and A. Kaya,”Effect of parameter selection on fuzzy clustering”. Mehmet Akif Ersoy University Applied Scince Journal, vol. 2, pp. 22-33, 2018.
  • M. M. Chi, Q. Qian Q., and J. A. Benediktsson “Cluster-based ensemble classification for hyperspectral remote sensing”, in Proc. IEEE IGARSS, 2018, 209–212.
  • P. Kamavisdar, et al., “A Survey on Image Classification Approaches and Techniques”, International Journal of Advanced Research in Computer and Communication Engineering, vol. 2, Issue 1, 2008.
  • J. C. Bezdek, “Pattern recognition with fuzzy objective function algorithms: Plenum”, New York, 1982, pp. 256.
  • D. L. Pham, J. L. Prince, “Adaptive Fuzzy Segmentation of Magnetic Resonance Images”, IEEE Trans. Medical Imaging, vol. 18, pp. 737–752, 1999.
  • N. Dhanachandra, K. Manglem and Y. J. Chanu, “Image segmentation using k-means clustering algorithm and subtractive clustering algorithm”, Procedia Computer Science, vol.54, pp.764-771, 2015.
  • C. Ambroise, G. Govaert, “Convergence of an EM-type algorithm for spatial clustering”, Pattern Recognition Letters vol. 19, pp. 919–927, 1998.
  • T McInerney and D. Terzopoulos, “Deformable models in medical image analysis: A survey”, Medical Image Analysis, vol. 1, no. 2, pp. 91-108, 1996.
  • D. Q. Zhang. and S. C. Chen, “Clustering in completed data using Kernelbased fuzzy cmeans algorthim”, Neural Processing Letters, vol.18, no. 3, pp. 155-162, 2003.
  • F. Meyer and S. Beucher, “Morphology segmentation”, Journal Visual Communications and Image Representation, vol. 1, no. 1, pp. 21-26, 1990.
  • H. Shamsi, et al, “A modified fuzzy c means clustering with spatial information for image segmentation” International journal of computer theory and engineering, IACSIT, 2012.
  • A. Khwairakpam, D. Kandar, D., B. Paul, “Noise reduction in synthetic aperture radar images using fuzzy logic and genetic algorithm. Microsyst Technol vol. 25,pp. 1743–1752, 2019.
  • J. Zhang and Z. Ma, “Hybrid Fuzzy Clustering Method Based on FCM and Enhanced Logarithmical PSO” Computational Intelligence and Neuroscience, vol.2020, 2020.
  • R. Duda, and P. Hart, “Pattern classification and scene analysis”, Wiley-Interscience, New York, 1973.
  • A. Seker A. and A. G. Yuksek, “Stacked Autoencoder Method for Fabric Defect Detection” Cumhuriyet Science Journal., vol. 38, no. 2, 2017.
  • V. N. Vapnik, “The Nature of Statistical Learning Theory” Springer-Verlag, New York, 1995.
Toplam 17 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Research Article
Yazarlar

Rıdvan Saraçoğlu 0000-0002-5853-4545

Nooshin Nemati 0000-0002-5306-0344

Yayımlanma Tarihi 1 Ekim 2020
Yayımlandığı Sayı Yıl 2020

Kaynak Göster

APA Saraçoğlu, R., & Nemati, N. (2020). Vehicle Detection Using Fuzzy C-Means Clustering Algorithm. International Journal of Applied Mathematics Electronics and Computers, 8(3), 85-91. https://doi.org/10.18100/ijamec.799431
AMA Saraçoğlu R, Nemati N. Vehicle Detection Using Fuzzy C-Means Clustering Algorithm. International Journal of Applied Mathematics Electronics and Computers. Ekim 2020;8(3):85-91. doi:10.18100/ijamec.799431
Chicago Saraçoğlu, Rıdvan, ve Nooshin Nemati. “Vehicle Detection Using Fuzzy C-Means Clustering Algorithm”. International Journal of Applied Mathematics Electronics and Computers 8, sy. 3 (Ekim 2020): 85-91. https://doi.org/10.18100/ijamec.799431.
EndNote Saraçoğlu R, Nemati N (01 Ekim 2020) Vehicle Detection Using Fuzzy C-Means Clustering Algorithm. International Journal of Applied Mathematics Electronics and Computers 8 3 85–91.
IEEE R. Saraçoğlu ve N. Nemati, “Vehicle Detection Using Fuzzy C-Means Clustering Algorithm”, International Journal of Applied Mathematics Electronics and Computers, c. 8, sy. 3, ss. 85–91, 2020, doi: 10.18100/ijamec.799431.
ISNAD Saraçoğlu, Rıdvan - Nemati, Nooshin. “Vehicle Detection Using Fuzzy C-Means Clustering Algorithm”. International Journal of Applied Mathematics Electronics and Computers 8/3 (Ekim 2020), 85-91. https://doi.org/10.18100/ijamec.799431.
JAMA Saraçoğlu R, Nemati N. Vehicle Detection Using Fuzzy C-Means Clustering Algorithm. International Journal of Applied Mathematics Electronics and Computers. 2020;8:85–91.
MLA Saraçoğlu, Rıdvan ve Nooshin Nemati. “Vehicle Detection Using Fuzzy C-Means Clustering Algorithm”. International Journal of Applied Mathematics Electronics and Computers, c. 8, sy. 3, 2020, ss. 85-91, doi:10.18100/ijamec.799431.
Vancouver Saraçoğlu R, Nemati N. Vehicle Detection Using Fuzzy C-Means Clustering Algorithm. International Journal of Applied Mathematics Electronics and Computers. 2020;8(3):85-91.