Comparative Study for Color Image Clustering based on Mahalanobis Distance Method

(Improved fuzzy C- mean clustering method)


  • Fadhil Hanoon Abbood collage of education-Almustansiriyah university, Iraq



Image Clustering, Fuzzy C- Mean, Mahalanobis, Euclidean Distances


Color image has the potential to convey more information than monochrome or gray level images, RGB color model is used in many applications of image processing and image analysis such as Image Segmentation. The standard ways to deal with picture investigation and acknowledgment creatures by division of the picture into areas (articles) and figuring different properties and connections among these districts. Image segmentation algorithms, have been developed for extracting these regions. Important image segmentation is difficult to process due to the inherent noise and deterioration of the input indicators of the algorithm. The regions are not always specified, however, and it is often more fitting to consider them as fuzzy subjects in the image. In this work, an algorithm that is used to segment color images with clustering methods is defined. The lower conditions, results and results are illustrated. In the clustering algorithm, the results are compared using both Mahalanobis and Euclidean distances.


Schmid, P.: Image segmentation by color clustering, http://www.schmid-,2011

Neary D., “Fractal Methods in Image Analysis and Coding“, M. Eng. thesis, Dublin City University, 2002.

Srikanteswara S., “Feature Identification in Wooden Boards Using Color Image Segmentation”, M. Sc Thesis, State University, Electrical and Computer Engineering, 1997.

Young I.T., Gerbrands J.J., and . van Vliet L.J,” Image Processing Fundamentals”, Netherlands Organization for Scientific Research (NWO) Grant 900-538-040, 1998.

Baogang W., Dongming L., Yunhe P., and Wenhua X., “Interactive Image Segmentation Using Multiple Color Spaces and Its Application in Ancient Art Preservation”, Artificial Intelligence Institute, Zhejiang University, Hangzhou, 2000, China P. R. 310027,

Zhao B., ”Color Space”, Electrical Engineering,SUNY,NY, 2002,

Moore R., “Digital Image Processing “, Mathematics Department, Macquarie University, Sydney, 1999.

Scott .E. Umbaugh, ''Computer Vision & Image Processing: A practical Approach Using CVIP tools '', Prentice Hall. Inc. 1998.

Gonzalez c. Rafael , Richard E. Woods" Digital Image processing " Addison-Wesley, 2002.

Rastislav Lukac , Konstantinos N. Plataniotis " Color Image Processing – Method and Application " University of Toronto

karbek W. ,and Koschan A., “Colour Image Segmentation :A Survey”, Technical University of Berlin, 1994.

Wesolkowski S. B., “Color Image Edge Detection and Segmentation: A Comparison of the Vector Angle and the Euclidean Distance Color Similarity Measures “, M. Sc. Thesis, University of Waterloo, 1999.

Ilic S. , and Ulicny B., ”Seeded Region Growing Method for Image Segmentation”, the Swiss Federal Institute of Technology, 2000.

Sadiq A Mehdi, Khalid Kadhim Jabbar, Fadhil Hanoon Abbood “Image Encryption Based on The Nove15D Hyper-Chaotic System Via Improved AES Algorithm” International Journal of Civil Engineering and Technology, Publication date 2018.

Rana Saad Mohammed, Fadhil Hanoon Abbood, Intisar Abid Yousif “Image encryption technique using clustering and stochastic standard map” Conference :2016 Al-Sadeq International Conference on Multidisciplinary in IT and Communication Science and Applications (AIC-MITCSA) IEEE, 2016/5/9.

Fadhil Hanoon Abbood, Rana Saad Mohammed, Intisar Abid Yousif “Random Chaotic Number Generation based Clustered Image Encryption” International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2763 Issue 03, Volume 3 (March 2016)



How to Cite

Fadhil Hanoon Abbood. (2020). Comparative Study for Color Image Clustering based on Mahalanobis Distance Method: (Improved fuzzy C- mean clustering method). I. J. Of Advances in Scientific Research and Engineering-IJASRE (ISSN: 2454 - 8006), 6(10), 72-78.