Rotation-invariant method for texture matching using model-based histograms and GLCM Online publication date: Sun, 27-Aug-2017
by Izem Hamouchene; Saliha Aouat
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 9, No. 1, 2017
Abstract: Nowadays, researchers are interested in informatics systems that process automatically the information. Image is an interesting research area due to the growth of the technologies. In this paper, we have proposed a new texture analysis method. One of the key problems in image processing is the rotation. Therefore, the proposed method is robust against rotation. The goal of this study is to construct a model from each texture. After that, the system classifies the query texture based on the extracted texture models. In this work, we applied a recent and efficient feature extraction method called rotation invariant neighbourhood-based binary pattern (RINBP). The proposed system combines between two parts. First, extract the RINBP model from the texture. Second, we apply the GLCM method in order to extract statistical measures. In the experiments, we have used the Brodats album database. Experimental parts illustrate the efficiency and the robustness of the proposed system against rotation.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Reasoning-based Intelligent Systems (IJRIS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook