A latent semantic analysis-based image tag optimisation method
by Aiping Cai
International Journal of Applied Decision Sciences (IJADS), Vol. 13, No. 1, 2020

Abstract: According to the nominal tags, the adverb and adjective high-level semantic tags which is described human emotion are easily to exceed the handling scope. Furthermore, the majority of optimisation methods exists many problems (e.g., although inputting a high application cost, it cannot get a satisfactory effect). This paper proposed an effective image tag optimisation algorithm which was composed of two important parts: firstly, a random walk model was used to process the initial image tag information, the image relationship diagram and the tag relationship diagram were structured based on image vision similarity and tag relevancy respectively, then, the image and tag information was spread through a dual-diagram (image relationship diagram and tag relationship diagram) walk mode randomly; secondly, a new image tag optimisation model was built according to three factors, i.e., semantic uniformity, noise sparsity and result matrix sparsity. In the experimental stage, the effectiveness of this model and the superiority of pre-processing were verified by experiment. Compared to other methods, the experimental result indicates that this algorithm is more reasonable and efficient.

Online publication date: Thu, 02-Jan-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 Applied Decision Sciences (IJADS):
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