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Study the Effect of Convolutional Local Information-Based Fuzzy c-Means Classifiers with Different Distance Measures

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Abstract

For improving image attributes and increase robustness to handle the noise for image classification, fuzzy algorithms were introduced. The fuzzy c-means (FCM) algorithm has not been suggested to incorporate spectral and spatial neighborhood information for accomplishing image classification. In this study, evolution developed for overall accuracy (OA) calculated by FERM (fuzzy error matrix) technique for an algorithm, namely fuzzy local information c-means (FLICM) using base classifier FCM, adaptive fuzzy local information c-means (ADFLICM) using base classifier FCM and fuzzy c-means with constraints. For these algorithms, various distance measures and weighting components (m) are studied. This study provides a comparison among different forms of local convolution with an FCM classifier, with a combination of different distance measures and weighting components to select the best classification approach. FLICM using base classifier FCM and Euclidean distance measures with a weighting component (m) of 1.3 has resulted in the highest overall accuracy compared to other classifiers used with various distance measures.

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References

  • Agarwal, S., Burgess, C., & Crammer, K. (2009). Advances in Ranking. In Workshop, Twenty-Third Annual Conference on Neural Information Processing Systems. Whistler, BC.

  • Ahmed, M. N., Yamany, S. M., Farag, A. A., & Moriarty, T. (1999). Bias field estimation and adaptive segmentation of MRI data using a modified fuzzy C-means algorithm. In Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) (Vol. 1, pp. 250–255). IEEE.

  • Baccour, L., & John, R. I. (2015). Experimental analysis of crisp similarity and distance measures. In 6th Int. Conf. Soft Comput. Pattern Recognition, SoCPaR (no. 2, pp. 96–100).

  • Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2–3), 191–203.

    Article  Google Scholar 

  • Bray, J. R., & Curtis, J. T. (1957). An ordination of the upland forest communities of Southern Wisconsin. Ecological Monographs, 27(4), 325–349.

    Article  Google Scholar 

  • Cai, W., Chen, S., & Zhang, D. (2007). Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognition, 40(3), 825–838.

    Article  Google Scholar 

  • Choodarathnakara, A. L., Kumar, T. A., & Koliwad, S. (2012). Mixed pixels: A challenge in remote sensing data classification for improving performance. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 1(9), 2278.

    Google Scholar 

  • Dagher, I., & Issa, S. (2012). Subband effect of the wavelet fuzzy C-means features in texture classification. Image and Vision Computing, 30(11), 896–905.

    Article  Google Scholar 

  • Dunn, J. C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3(3), 32–57.

    Article  Google Scholar 

  • Foody, G. M., Lucas, R. M., Curran, P. J., & Honzak, M. (1997). Non-linear mixture modeling without end-members using an artificial neural network. International Journal of Remote Sensing, 18(4), 937–953.

    Article  Google Scholar 

  • Hasnat, A., Halder, S., Bhattacharjee, D., Nasipuri, M., & Basu, D. K. (2013). Comparative study of distance metrics for finding skin color similarity of two color facial images. ACER: New Taipei City, Taiwan, 99–108.

  • Hathaway, R. J., Bezdek, J. C., & Hu, Y. (2000). Generalized fuzzy c-means clustering strategies using Lpnorm distances. IEEE Transactions on Fuzzy Systems, 8(5), 576–582.

    Article  Google Scholar 

  • Hore, P., Hall, L. O., & Goldgof, D. B. (2007). Creating streaming iterative soft clustering algorithms. In Fuzzy Information Processing Society, 2007. NAFIPS’07. Annual Meeting of the North American (pp. 484–488).

  • Kanellopoulos, I., Varfis, A., Wilkinson, G. G., & Megier, J. (1992). Land-cover discrimination in SPOT HRV imagery using artificial neural networks 20-class experiment. International Journal of Remote Sensing, 13(5), 917–924.

    Article  Google Scholar 

  • Kaymak, U., & Setnes, M. (2000). Extended fuzzy clustering algorithms. ERIM Report Series Reference No. ERS-2001-51-LIS.

  • Kerdiles, H., & Grondona, M. O. (1995). NOAA-AVHRR NDVI decomposition and subpixel classification using linear mixing in the Argentinean Pampa. International Journal of Remote Sensing, 16(7), 1303–1325.

    Article  Google Scholar 

  • Krinidis, S., & Chatzis, V. (2010). A robust fuzzy local information C-means clustering algorithm. IEEE Transactions on Image Processing, 19(5), 1328–1337.

    Article  Google Scholar 

  • Leski, J. (2003). Towards a robust fuzzy clustering. Fuzzy Sets and Systems, 137(2), 215–233.

    Article  Google Scholar 

  • Nandan, R., Kamboj, A., Kumar, A., Kumar, S., & Venkata Reddy, K. (2016). Formosat-2 with Landsat-8 temporal—multispectral data for wheat crop identification using Hypertangent Kernel-based Possibilistic classifier. Journal of Geomatics, 10, 89–95.

    Google Scholar 

  • Okeke, F., & Karnieli, A. (2006). Methods for fuzzy classification and accuracy assessment of historical aerial photographs for vegetation change analyses. Part I: Algorithm development. International Journal of Remote Sensing, 27(1), 153–176.

    Article  Google Scholar 

  • Scollar, I., Weidner, B., & Huang, T. S. (1984). Image enhancement using the median and the interquartile distance. Computer Vision, Graphics, and Image Processing, 25, 236–251.

    Article  Google Scholar 

  • Senoussaoui, M., Kenny, P., Stafylakis, T., & Dumouchel, P. (2014). A study of the cosine distance-based mean shift for telephone speech diarization. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(1), 217–227.

    Article  Google Scholar 

  • Vassiliadis, S., Hakkennes, E. A., Wong, J. S. S. M., & Pechanek, G. G. (1998). The sum-absolute-difference motion estimation accelerator. In Proc.—24th EUROMICRO Conf. EURMIC (vol. 2, pp. 559–566).

  • Zhang, H., Wang, Q., Shi, W., & Hao, M. (2017). A novel adaptive fuzzy local information C-means clustering algorithm for remotely sensed imagery classification. IEEE Transactions on Geoscience and Remote Sensing, 55(9), 5057–5068.

    Article  Google Scholar 

  • Zhang, M., Therneau, T., McKenzie, M. A., Li, P., & Yang P. (2008). A fuzzy c-means algorithm using correlation metrics and gene ontology. In 19th Int. Conf. Pattern Recognit. (pp. 1–4).

  • Zhao, F. (2013). Fuzzy clustering algorithms with self-tuning non-local spatial information for image segmentation. Neurocomputing, 106, 115–125.

    Article  Google Scholar 

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Acknowledgements

The authors would like to express their sincere gratitude to Department of Mining Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad for their help and support, and also to Indian Institute of Remote Sensing Dehradun for their kind co-operation during the course of this study.

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Correspondence to Shilpa Suman.

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Suman, S., Kumar, D. & Kumar, A. Study the Effect of Convolutional Local Information-Based Fuzzy c-Means Classifiers with Different Distance Measures. J Indian Soc Remote Sens 49, 1561–1568 (2021). https://doi.org/10.1007/s12524-021-01333-6

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