Salient object detection of social images based on semantic tag context
by Ye Liang; Congyan Lang; Jian Yu; Hongzhe Liu
International Journal of Sensor Networks (IJSNET), Vol. 23, No. 4, 2017

Abstract: Salient object detection is an important process for machines to understand visual contents as humans. Typically, most previous studies on salient object detection infer salient map by only using the visual features. In this paper, we propose a new paradigm on salient object detection, which aims at producing more reliable results by mining the context information from user's annotated tags. To address this problem, we firstly construct a large scale salient object dataset, which includes 5429 images from the NUSWIDE dataset (a real world web image database from National University of Singapore) with tag information and accurate human-labeled masks. Moreover, a specialised conditional random field (CRF) model is also proposed which takes account of both tag contexts and appearance cues. Extensive experiments show the effectiveness and superiority of our model which can accurately locate the salient objects.

Online publication date: Sun, 09-Apr-2017

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 Sensor Networks (IJSNET):
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