Salient object detection via multiple saliency weights

Salient object detection via multiple saliency weights Salient object detection aims to emulate the extraordinary capability of human visual system, which has the ability to find the most visually attractive objects in a complex visual scene. The human visual attention is often complicated and affected by many factors. In this paper, we present a novel bottom-up approach to automatically detect salient objects of an image via multiple visual cues. The key idea of our approach is to represent a saliency map of an image as an integration of multiple visual cues (saliency weights), which have been proven to be effective and useful. Specifically, we propose four saliency weights, i.e., local contrast weight, superpixel clarity weight, background probability weight, and central bias weight, to effectively represent each visual cue. To obtain our saliency map, the four resulting saliency weights are integrated in a principled way via multiplication and summation based fusion. Furthermore, we propose a new superpixel-level saliency smoothing approach to optimize the integrated results for producing clean and consistent saliency maps. Our experimental results on three standard benchmark datasets demonstrate that the proposed approach outperforms other saliency detection approaches in terms of the subjective observations and objective evaluations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Salient object detection via multiple saliency weights

Loading next page...
 
/lp/springer_journal/salient-object-detection-via-multiple-saliency-weights-TUqs5M0sDh
Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-017-4725-7
Publisher site
See Article on Publisher Site

Abstract

Salient object detection aims to emulate the extraordinary capability of human visual system, which has the ability to find the most visually attractive objects in a complex visual scene. The human visual attention is often complicated and affected by many factors. In this paper, we present a novel bottom-up approach to automatically detect salient objects of an image via multiple visual cues. The key idea of our approach is to represent a saliency map of an image as an integration of multiple visual cues (saliency weights), which have been proven to be effective and useful. Specifically, we propose four saliency weights, i.e., local contrast weight, superpixel clarity weight, background probability weight, and central bias weight, to effectively represent each visual cue. To obtain our saliency map, the four resulting saliency weights are integrated in a principled way via multiplication and summation based fusion. Furthermore, we propose a new superpixel-level saliency smoothing approach to optimize the integrated results for producing clean and consistent saliency maps. Our experimental results on three standard benchmark datasets demonstrate that the proposed approach outperforms other saliency detection approaches in terms of the subjective observations and objective evaluations.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: May 18, 2017

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off