Pattern analysis and texture classification using finite state automata scheme Online publication date: Mon, 14-Oct-2019
by B. Eswara Reddy; Ramireddy Obulakonda Reddy; E. Keshava Reddy
International Journal of Advanced Intelligence Paradigms (IJAIP), Vol. 14, No. 1/2, 2019
Abstract: The paper proposes a complete modelling of finite state automata along with the associated classifier for texture classification. Pattern analysis of the texture image is performed by proposing a symbolic pattern-based algorithm. This algorithm is developed based on the symbolic dynamics and finite state automata theory for estimating the state transition of the texture variations. Texture image is divided into several partitions, i.e., texture, background of the texture, shadow of the texture, etc. Finite automata state transitions are used to extract the features from the symbolised image. A binary classifier is designed to classify the texture categories based on the feature extraction from the finite automata theory. Pattern analysis is performed on the KITH-TIPS dataset for ten varied categories of texture. 99.12% classification accuracy is achieved when compared with other state-of-art techniques. The experimental study shows the better efficiency of the proposed system when compared to other existing methods.
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 Advanced Intelligence Paradigms (IJAIP):
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