Improvement of accuracy in batik i mage classification due to scale and rotation changes using M2ECS-LBP algorithm

This research evolves feature extraction algorithms in overcoming difficulties in classifying batik images that
encounter changes in scale and rotation. the algorithm is multiscale and multilevel extended center symmetric
local binary pattern (M2ECS-LBP). In utilizing this algorithm using several types of windows to obtain
optimal feature extraction results, ranging from the size of 6x6, 9x9, 12 x 12 and 15x15 or a combination of
several windows. However, for the use of algorithm carried out sequentially, it also requires a special strategy
to obtain optimal image feature extraction results to support performance accuracy in the classification. The
results of classification accuracy using K-Nearest neighborhood had reached up until the percentage to 78,4
– 81.7 percent of the image undergoing changes in scale and rotation. However, if the batik image undergoes
a change in scale but the rotation is the same then the accuracy percentage can reach 98-99 percent. This
algorithm is a very powerful breakthrough with lighter computing techniques than other algorithms. This
research can be continued to recognize moving images, expected with maximum accuracy.