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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

Tan, Weimin; Yan, Bo
Multimedia Tools and Applications , Volume 76 (23) – May 18, 2017
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References (22)

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
DOI
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 Applications – Springer Journals

Published: May 18, 2017

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