Open AccessAn Efficient Image Retrieval System using GLCM Features and Kullback Leibler Divergence
Author(s) -
Ch. Kodanda Ramu,
T. Mahalakshmi
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.c6062.029320
Subject(s) - kullback–leibler divergence , divergence (linguistics) , artificial intelligence , computer science , pattern recognition (psychology) , mixture model , gray level , set (abstract data type) , image (mathematics) , gaussian , data set , computer vision , philosophy , linguistics , physics , quantum mechanics , programming language
Image processing is a process of extracting features from an image. The features of the image are extracted using the correlation model, based on Gray-Level Co-Occurrence Matrix (GLCM). Each of the images considered for data set are converted into gray level before applying Gaussian Mixture Model (GMM). The features extracted from GLCM are given as an input to the model-based technique so that the relative Probability Density Functions (PDF) are extracted. The comparison is carried out in the same manner by identifying the relative PDF of the training set and test data by using KullbackLeibler divergence method (KL-Divergence). In this paper an attempt is made for developing an effective model to retrieve the images based on features by considering GLCM and GMM. The results derived show that the proposed methodology is able to retrieve images more effectively.