Region-based image retrieval using the semantic cluster matrix and adaptive learning
by I. Felci Rajam; S. Valli
International Journal of Computational Science and Engineering (IJCSE), Vol. 7, No. 3, 2012

Abstract: An image retrieval system, which reduces the semantic gap between the low level image features and the high level semantic concepts, using the semantic cluster matrix (SCM) and adaptive learning during testing, is proposed. This mechanism retrieves semantically relevant images for both trained and untrained image categories. The SCM groups the new categories into semantic clusters, records the cluster's semantic information, gets the relevance feedback (RF) from the user, and records this in the SCM. Thus, the SCM adaptively learns about the new categories during the testing time, and is able to retrieve semantically relevant images for untrained categories also. Experiments were conducted using the Caltech image dataset consisting of 101 categories of images. The obtained results demonstrate that the proposed approach achieves good performance in terms of retrieval accuracy.

Online publication date: Mon, 22-Sep-2014

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 Computational Science and Engineering (IJCSE):
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