Multimedia auto-annotation via label correlation mining
by Feng Tian; Fuhua Shang; Ning Sun
International Journal of High Performance Computing and Networking (IJHPCN), Vol. 13, No. 4, 2019

Abstract: How to automatically determine the label for multimedia object is crucial for multimedia retrieval. The neighbour voting mechanism is known to be effective for multimedia object annotation. However, it estimates the relevance of a label with respect to multimedia content by labels' frequency derived from its nearest neighbours, which does not take into account the assigned label set as a whole. We propose LSLabel, a novel algorithm that achieves comparable results with label correlation mining. By incorporating the label correlation and label relevance with respect to multimedia content, the problem of assigning labels to multimedia object is formulated into a joint framework. The problem can be efficiently optimized in a heuristic manner, which allows us to incorporate a large number of feature descriptors efficiently. On two standard real world datasets, we demonstrate that LSLabel matches the current state-of-the-art.

Online publication date: Wed, 24-Apr-2019

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 High Performance Computing and Networking (IJHPCN):
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