About this Computer Science article

Textural Features for Image Classification by Robert M. Haralick; K. Shanmugam; Its'Hak Dinstein is a Computer Science article available to read on EtoBox.

Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray- tone spatial dependancies, and illustrates their application in categoryidentification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Tech- nology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a gener

Author

Robert M. Haralick; K. Shanmugam; Its'Hak Dinstein

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

1973

Language

EN

Field

Computer Science (Physical Sciences)