NDLI: Evaluating salient object detection in natural images with multiple objects having multi-level saliency

Content Provider	IET Digital Library
Author	Yildirim, Gökhan Sen, Debashis Kankanhalli, Mohan Süsstrunk, Sabine
Abstract	Salient object detection is evaluated using binary ground truth (GT) with the labels being salient object class and background. In this study, the authors corroborate based on three subjective experiments on a novel image dataset that objects in natural images are inherently perceived to have varying levels of importance. The authors' dataset, named SalMoN (saliency in multi-object natural images), has 588 images containing multiple objects. The subjective experiments performed record spontaneous attention and perception through eye fixation duration, point clicking and rectangle drawing. As object saliency in a multi-object image is inherently multi-level, they propose that salient object detection must be evaluated for the capability to detect all multi-level salient objects apart from the salient object class detection capability. For this purpose, they generate multi-level maps as GT corresponding to all the dataset images using the results of the subjective experiments, with the labels being multi-level salient objects and background. They then propose the use of mean absolute error, Kendall's rank correlation and average area under precision–recall curve to evaluate existing salient object detection methods on their multi-level saliency GT dataset. Approaches that represent saliency detection on images as local-global hierarchical processing of a graph perform well in their dataset.
Starting Page	2249
Ending Page	2262
Page Count	14
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 10, Aug (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0787
Journal	IET Image Processing
Publisher	The Institution of Engineering and Technology
Publisher Date	2019-11-15
Access Restriction	Open
Rights License	Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/3.0/)
Subject Keyword	Computer Vision And Image Processing Technique Dataset Image Image Dataset Image Segmentation Multilevel Maps Multilevel Saliency GT Dataset Multilevel Salient Objects Multiobject Image Multiobject Natural Image Object Detection Object Saliency Optical, Image And Video Signal Processing Salient Object Class Detection Capability Statistical Analysis Statistics Visual Perception
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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