Open AccessStone Image Classification Based on Overlapped 5-bit T-Patterns occurrence on 5-by-5 Sub Images
Author(s) -
Palnati Vijay Kumar,
Pullela S V V S R Kumar,
Nakkella Madhuri,
M. Uma Devi
Publication year - 2016
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v6i3.pp1152-1160
Subject(s) - computer science , artificial intelligence , texture (cosmology) , window (computing) , pattern recognition (psychology) , digital image , contextual image classification , image (mathematics) , computer vision , image processing , operating system
Texture classification is widely used in understanding the visual patterns and has wide range of applications. The present paper derived a novel approach to classify the stone textures based on the patterns occurrence on each sub window. The present approach identifies overlapped nine 5 bit T-patterns (O5TP) on each 5×5 sub window stone image. Based the number of occurrence of T-patterns count the present paper classify the stone images into any of the four classes i.e. brick, granite, marble and mosaic stone images. The novelty of the present approach is that no standard classification algorithm is used for the classification of stone images. The proposed method is experimented on Mayang texture images, Brodatz textures, Paul Bourke color images, VisTex database, Google color stone texture images and also original photo images taken by digital camera. The outcome of the results indicates that the proposed approach percentage of grouping performance is higher to that of many existing approaches.