Saliency detection using hierarchical manifold learning

Qiu, Youhai, Sun, Xiangping and She, Mary Fenghua 2015, Saliency detection using hierarchical manifold learning, Neurocomputing, vol. 168, pp. 538-549, doi: 10.1016/j.neucom.2015.05.073.

Attached Files
Name Description MIMEType Size Downloads

Title Saliency detection using hierarchical manifold learning
Author(s) Qiu, Youhai
Sun, Xiangping
She, Mary FenghuaORCID iD for She, Mary Fenghua
Journal name Neurocomputing
Volume number 168
Start page 538
End page 549
Total pages 12
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015-11-30
ISSN 0925-2312
Keyword(s) Entropy
Hierarchical sequence
Manifold learning
Summary Saliency detection is critical to many applications in computer vision by eliminating redundant backgrounds. The saliency detection approaches can be divided into two categories, i.e., top-down and bottom-up. Among them, bottom-up models have attracted more attention due to their simple mechanisms. However, many existing bottom-up models are not robust to crowded backgrounds because of missing salient regions within feedforward frameworks which is often not effective for complex scenes. We tackle these problems by modifying and extending a bottom-up saliency detection model through three phases, (1) constructing a hierarchical sequence of images from the perspective of entropy, (2) estimated mid-level cues are used as feedback information, (3) subsequently generating saliency maps by global context and local uniqueness in a graph-based framework. We also compare the proposed bottom-up model with state-of-the-art approaches on two benchmark datasets to evaluate its saliency detection performance. The experimental results demonstrate that the proposed bottom-up saliency detection approach is not only robust to both cluttered and clean scenes, but also able to obtain objects with different scales.
Language eng
DOI 10.1016/j.neucom.2015.05.073
Field of Research 080104 Computer Vision
080106 Image Processing
080109 Pattern Recognition and Data Mining
08 Information And Computing Sciences
09 Engineering
17 Psychology And Cognitive Sciences
Socio Economic Objective 890201 Application Software Packages (excl. Computer Games)
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Elsevier
Persistent URL

Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 10 times in TR Web of Science
Scopus Citation Count Cited 11 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 365 Abstract Views, 5 File Downloads  -  Detailed Statistics
Created: Mon, 29 Feb 2016, 09:52:48 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact