M.S. Priya, St.Anne’s First Grade College for Women, Bengaluru, India.
G. M. Kadhar Nawaz, Sona College of Technology, Salem, India.
DOI : 01.0401/ijaict.2016.11.11
International Journal of Advanced Information and Communication Technology
Received On : May 12, 2019
Revised On : June 13, 2019
Accepted On : July 10, 2019
Published On : August 05, 2019
Volume 06, Issue 08
Pages : 1121-1126
Abstract
Being the most successful methods for texture discrimination the Spatial Gray Level Dependence Method, Run Difference Method and Local Binary Pattern method we have investigated its effectiveness in extracting features to classify or categorize an image. The Spatial Gray Level Dependence aspect of texture is concerned with the spatial distribution and spatial dependence among the gray levels in a local area. RDM is similar to SGLDM which extracts features that describe the size and prominence of texture elements in the image.LBP features can provide robustness against variation in illumination. The extracted features such as contrast, correlation, homogeneity, energy, sharpness facilitates the subsequent learning leading to a better interpretation of the image. K-nn is a non-parametric method used for classification and regression. We classify the image using K-Nearest Neighbour Algorithm from the LBP image output which helps to represent the details with more significance.
Keywords
SGLDM, GLCM, LBP, RDM, K-nn.
Cite this article
M.S. Priya,G. M. Kadhar Nawaz, “MATLAB based Feature Extraction and Clustering Images Using K-Nearest Neighbour Algorithm” INTERNATIONAL JOURNAL OF ADVANCED INFORMATION AND COMMUNICATION TECHNOLOGY, pp.1121-1126, August 05, 2019.
Copyright
© 2019 M.S. Priya, G. M. Kadhar Nawaz. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.