비균일 양자화 기법에 기반을 둔 GLCM의 성능개선

Abstract

Abstract This paper presents a performance improvement of gray level co- occurrence matrix(GLCM) based on the nonuniform quantization, which is generally used to analyze the texture of images. The nonuniform quantization is given by Lloyd algorithm of recursive technique by minimizing the mean square error. The nonlinear intensity levels by performing nonuniformly the quant ization of image have been used to decrease the dimension of GLCM, that is applied to reduce the computation loads as a results of generating the GLCM and calculating the texture parameters by using GLCM. The propo sed method has been applied to 30 images of 120*120 pixels with 256-gray level for analyzing the texture by calculating the 6 parameters, such as angular second moment, contrast, variance, entropy, correlation , inverse difference moment. The experimental results show that the proposed method has a superior computatio n time and memory to the conventional 256-level GLCM method without performing the quantization. Espe cially, 16-gray level by using the nonuniform quantization has the superior performance for analyzing texture s to another levels of 48, 32, 12, and 8 levels.Key Words : Nonuniform Quantization, Lloyd Algorithm, Gray Level Co-occur rence Matrix(GLCM), Texture Analysis, Image AnalysisReceived: Sep. 14, 2014Revised : Sep. 28, 2014Accepted: Feb. 9, 2015