Global contrast based salient region detection

Cheng, Ming-Ming, Mitra, Niloy J., Huang, Xiaolei, Torr, Philip H. S. and Hu, Shi-Min ORCID: https://orcid.org/0000-0001-7507-6542 2015. Global contrast based salient region detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 37 (3) , pp. 569-582. 10.1109/TPAMI.2014.2345401

Full text not available from this repository.

Abstract

Automatic estimation of salient object regions across images, without any prior assumption or knowledge of the contents of the corresponding scenes, enhances many computer vision and computer graphics applications. We introduce a regional contrast based salient object detection algorithm, which simultaneously evaluates global contrast differences and spatial weighted coherence scores. The proposed algorithm is simple, efficient, naturally multi-scale, and produces full-resolution, high-quality saliency maps. These saliency maps are further used to initialize a novel iterative version of GrabCut, namely SaliencyCut, for high quality unsupervised salient object segmentation. We extensively evaluated our algorithm using traditional salient object detection datasets, as well as a more challenging Internet image dataset. Our experimental results demonstrate that our algorithm consistently outperforms 15 existing salient object detection and segmentation methods, yielding higher precision and better recall rates. We also show that our algorithm can be used to efficiently extract salient object masks from Internet images, enabling effective sketch-based image retrieval (SBIR) via simple shape comparisons. Despite such noisy internet images, where the saliency regions are ambiguous, our saliency guided image retrieval achieves a superior retrieval rate compared with state-of-the-art SBIR methods, and additionally provides important target object region information.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
ISSN:	0162-8828
Date of First Compliant Deposit:	6 September 2016
Date of Acceptance:	24 July 2014
Last Modified:	01 Nov 2022 11:12
URI:	https://orca.cardiff.ac.uk/id/eprint/94203

Citation Data

Cited 1877 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item