Feature set processing using multi-objective optimisation algorithm to improve content-based image retrieval system
by B. Syam; Y. Srinivasa Rao
International Journal of Business Information Systems (IJBIS), Vol. 34, No. 2, 2020

Abstract: We propose a framework of genetic algorithms to search for Pareto optimal solutions (i.e., non-dominated solutions) of multi-objective optimisation problems. Our approach differs from single-objective genetic algorithms in its selection procedure and elite presence strategy. The selection procedure in our genetic algorithms selects individuals for a crossover operation based on a sum of multiple objective functions. The characteristic feature of the selection procedure is that the weights attached to the multiple objective functions are not constant but randomly specified for each selection. This might most likely decry the classification accuracy and increase noise once it extracts type content type pictures. To avoid these drawbacks, a brand new technique is planned to induce retrieval performance. The implementation results show the effectiveness of projected optimisation technique in retrieving all pictures. Furthermore, the performance of the proposed technique is evaluated by comparing with the other optimised CBIR methods.

Online publication date: Fri, 10-Jul-2020

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 Business Information Systems (IJBIS):
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