Abstract
The texture analysis plays an important role in image processing and image classification field. Texture is an important spatial feature useful for identifying objects in an image. The local binary pattern and entropy are the most popular statistical methods used in practice to measure the textural information of images. Here, we proposed new statistical approach for the classification of texture images. In this method, the local texture information 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.
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© 2011 Springer-Verlag Berlin Heidelberg
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Sabeenian, R.S., Dinesh, P.M. (2011). Texture Image Classification Using Gray Level Weight Matrix (GLWM). In: Das, V.V., Thomas, G., Lumban Gaol, F. (eds) Information Technology and Mobile Communication. AIM 2011. Communications in Computer and Information Science, vol 147. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20573-6_43
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DOI: https://doi.org/10.1007/978-3-642-20573-6_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20572-9
Online ISBN: 978-3-642-20573-6
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