Spatial texture feature classification algorithm for high resolution 3D images
by Ping Wang
International Journal of Information and Communication Technology (IJICT), Vol. 21, No. 3, 2022

Abstract: The existing feature classification algorithms have a lot of noise in the process of classification, which leads to the problems of low classification efficiency and unbalanced classification of image spatial texture features. Based on this, a texture feature classification algorithm based on RUSBoost is proposed. Wavelet coefficients, threshold processing and image reconstruction are used to denoise the image. On the basis of BDAWPSO algorithm, image segmentation is carried out by searching for the optimal threshold. Gabor transform and windowing are used to overcome the lack of local analysis ability and reduce the classification time. The original unbalanced image data is converted into new balanced data by using Rus boost algorithm. The experimental results show that the algorithm can improve the classification effect and display the texture information of the image better.

Online publication date: Wed, 14-Sep-2022

The full text of this article 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 Information and Communication Technology (IJICT):
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