The full text of this article

 

Enhancing the laws filter descriptor on DTCWT coefficients by thresholding approach for texture classification
by Sonali Dash; Uma Ranjan Jena
International Journal of Computational Vision and Robotics (IJCVR), Vol. 8, No. 6, 2018

 

Abstract: In this paper, we propose a new approach of combining dual-tree complex wavelet transform with traditional Laws' filter descriptor for texture classification using thresholding method. The dual-tree complex wavelet transform (DTCWT) is a recent technique to discrete wavelet transform, with important additional properties. It has been observed that the thresholding is a method to keep significant information of the image while discarding the unimportant part. On this basis for further enhancement of texture classification, we have acquired the texture images by applying thresholding technique to the entire texture database. These thresholded images are then applied to the fusion model of DTCWT with Laws' filter descriptor. We verify the effectiveness of the proposed method by utilising two texture databases such as Brodatz and UIUC. The proposed methods are compared with the classical Laws' filter descriptor. Results demonstrate that the proposed method greatly enhanced the classification accuracies by using k-NN as classifier.

Online publication date: Wed, 26-Sep-2018

 

is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

 
Pay per view:
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 Computational Vision and Robotics (IJCVR):
Login with your Inderscience username and password:

 

    Username:        Password:         

Forgotten your 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