期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2019
卷号：97
期号：14
页码：3859-3870
出版社：Journal of Theoretical and Applied
摘要：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.
关键词：Scale; Rotation; Classification; Batik; Multiscale And Multilevel; Similarity Measurement
