NDLI: Multi-scale contrast-based saliency enhancement for salient object detection

Content Provider	IET Digital Library
Author	Zhou, Wenhui Song, Teng Lin, Lili Lumsdaine, Andrew
Abstract	To achieve more complete and more uniformly highlighted salient object regions, this study presents a computational saliency enhancement model that incorporates the properties of multi-scale and logarithmic response into the local and global contrasts. A distinct feature of the authors model is a novel saliency enhancement operator. This operator can effectively enhance the saliency of object interior regions while simultaneously reducing blur on object boundaries caused by multiple scales. Their model is a general one that can make flexible tradeoffs between precision and recall. Detailed comparisons with 12 state-of-the-art methods show that their method can obtain satisfactory salient object regions that are closer to the human-labelled results. In addition, their method provides superior results in precision–recall, F-measure and mean absolute error.
Starting Page	207
Ending Page	215
Page Count	9
ISSN	17519632
Volume Number	8
e-ISSN	17519640
Issue Number	Issue 3, Jun (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/8/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2013.0118
Journal	IET Computer Vision
Publisher Date	2013-09-25
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Blur Reduction Computational Saliency Enhancement Model Computer Vision And Image Processing Technique F-measure Error Human-labelled Result Image Enhancement Image Restoration Logarithmic Response Property Mean Absolute Error Multiscale Contrast-based Saliency Enhancement Operator Object Boundary Object Detection Object Interior Region Optical, Image And Video Signal Processing Precision-recall Error Salient Object Detection
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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