Open AccessAn efficient method for image mining using GLCM and neural network
Author(s) -
T.R. Nisha Dayana,
A. Lenin Fred
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.33.13859
Subject(s) - computer science , artificial intelligence , artificial neural network , image (mathematics) , matlab , feature (linguistics) , abstraction , computer vision , image retrieval , pattern recognition (psychology) , content based image retrieval , function (biology) , data mining , philosophy , linguistics , epistemology , evolutionary biology , biology , operating system
Currently, content-based Image recovery (CBIR) drives for producing approaches which supports viable searching and scanning of vast picture progressive libraries by considering unwavering image texture features and has been a rapidly growing inspection bearing among image information recovery, computer vision, and database. The learning procedure of CBIR is achieved with the Neural Network method together with GLCM feature abstraction in our projected technique. Furthermore, with the ABC algorithm the normal/abnormal arrangement of the medical dataset images is managed. Lastly, to regulate the function of the projected method the solutions were replicated and associated with the available method. In the working platform of MATLAB, the projected method is applied.