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Saliency Boosting: a novel framework to refine salient object detection

Saliency Boosting: a novel framework to refine salient object detection Salient object detection is a challenging research area and various methods have been proposed in literature. However, these methods usually focus on detecting salient objects in particular type of images only and fail when exposed to a variety of images. Here, we address this problem by proposing a novel framework called Saliency Boosting for refining saliency maps. In particular, the framework trains an Artificial Neural Network Regressor to refine initial saliency measures which are obtained from existing saliency methods. Extensive experiments on seven publicly available datasets viz. MSRA10K-test, DUT-OMRON-test, ECSSD, PASCAL-S, SED2, THUR15K, and HKU-IS have been performed to determine the effectiveness of the proposed framework. The performance of the proposed framework is measured in terms of Precision, Recall, F-Measure, Precision–Recall curve, Overlapping Ratio, Area Under the Curve and Receiver Operating Characteristic curve. The proposed framework is compared with 20 state-of-the-art-methods including best performing methods in the last decade. Further, performance of the proposed framework is better than each individual saliency detection method used in the framework. The proposed framework outperforms or is comparable with 20 state-of-the-art methods in terms of the aforementioned performance measures on all datasets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Intelligence Review Springer Journals

Saliency Boosting: a novel framework to refine salient object detection

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References (45)

Publisher
Springer Journals
Copyright
Copyright © Springer Nature B.V. 2019
Subject
Computer Science; Artificial Intelligence; Computer Science, general
ISSN
0269-2821
eISSN
1573-7462
DOI
10.1007/s10462-019-09777-6
Publisher site
See Article on Publisher Site

Abstract

Salient object detection is a challenging research area and various methods have been proposed in literature. However, these methods usually focus on detecting salient objects in particular type of images only and fail when exposed to a variety of images. Here, we address this problem by proposing a novel framework called Saliency Boosting for refining saliency maps. In particular, the framework trains an Artificial Neural Network Regressor to refine initial saliency measures which are obtained from existing saliency methods. Extensive experiments on seven publicly available datasets viz. MSRA10K-test, DUT-OMRON-test, ECSSD, PASCAL-S, SED2, THUR15K, and HKU-IS have been performed to determine the effectiveness of the proposed framework. The performance of the proposed framework is measured in terms of Precision, Recall, F-Measure, Precision–Recall curve, Overlapping Ratio, Area Under the Curve and Receiver Operating Characteristic curve. The proposed framework is compared with 20 state-of-the-art-methods including best performing methods in the last decade. Further, performance of the proposed framework is better than each individual saliency detection method used in the framework. The proposed framework outperforms or is comparable with 20 state-of-the-art methods in terms of the aforementioned performance measures on all datasets.

Journal

Artificial Intelligence Review – Springer Journals

Published: Jun 11, 2020

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