TY  - JOUR
T1  - Supervised Classification of Images using Textural Features
AU - , S.S. Sreejamole AU - , L. Ganesan 
JO  - International Journal of Soft Computing
VL  - 4
IS  - 3
SP  - 122
EP  - 126
PY  - 2009
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2009.122.126
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2009.122.126
KW  - Texture spectrum
KW  -local binary pattern operator
KW  -entropy operator
KW  -images
KW  -statistical methods
KW  -spatial feature
AB  - Texture is an important spatial feature, useful for identifying objects or regions of interest in an image. The gray-level co-occurrence matrix approach is one of the most popular statistical methods used in practice to measure the textural information of images. Based on the proposed concept of texture unit, this study describes the local binary pattern, texture spectrum and entropy approach. In this method, the local texture in formation for a given pixel and its neighborhood is characterized by the corresponding texture unit and the global textural aspect of an image is revealed by its texture spectrum. The proposed method extracts the textural information of an image with a more complete respect of texture characteristics. A preliminary evaluation study demonstrates the potential usefulness of the proposed methods for texture analysis.
ER  -