Literature DB >> 22201056

Context-aware saliency detection.

Stas Goferman1, Lihi Zelnik-Manor, Ayellet Tal.   

Abstract

We propose a new type of saliency—context-aware saliency—which aims at detecting the image regions that represent the scene. This definition differs from previous definitions whose goal is to either identify fixation points or detect the dominant object. In accordance with our saliency definition, we present a detection algorithm which is based on four principles observed in the psychological literature. The benefits of the proposed approach are evaluated in two applications where the context of the dominant objects is just as essential as the objects themselves. In image retargeting, we demonstrate that using our saliency prevents distortions in the important regions. In summarization, we show that our saliency helps to produce compact, appealing, and informative summaries.

Mesh:

Year:  2012        PMID: 22201056     DOI: 10.1109/TPAMI.2011.272

Source DB:  PubMed          Journal:  IEEE Trans Pattern Anal Mach Intell        ISSN: 0098-5589            Impact factor:   6.226


  41 in total

1.  Automated choroidal neovascularization detection algorithm for optical coherence tomography angiography.

Authors:  Li Liu; Simon S Gao; Steven T Bailey; David Huang; Dengwang Li; Yali Jia
Journal:  Biomed Opt Express       Date:  2015-08-25       Impact factor: 3.732

2.  Robust Object Tracking Using Valid Fragments Selection.

Authors:  Jin Zheng; Bo Li; Peng Tian; Gang Luo
Journal:  Multimed Model       Date:  2016-01-03

3.  Automatic quantification of choroidal neovascularization lesion area on OCT angiography based on density cell-like P systems with active membranes.

Authors:  Jie Xue; Acner Camino; Steven T Bailey; Xiyu Liu; Dengwang Li; Yali Jia
Journal:  Biomed Opt Express       Date:  2018-06-20       Impact factor: 3.732

4.  Information-theoretic model comparison unifies saliency metrics.

Authors:  Matthias Kümmerer; Thomas S A Wallis; Matthias Bethge
Journal:  Proc Natl Acad Sci U S A       Date:  2015-12-10       Impact factor: 11.205

5.  What do saliency models predict?

Authors:  Kathryn Koehler; Fei Guo; Sheng Zhang; Miguel P Eckstein
Journal:  J Vis       Date:  2014-03-11       Impact factor: 2.240

6.  Comparative study of computational visual attention models on two-dimensional medical images.

Authors:  Gezheng Wen; Brenda Rodriguez-Niño; Furkan Y Pecen; David J Vining; Naveen Garg; Mia K Markey
Journal:  J Med Imaging (Bellingham)       Date:  2017-05-10

Review 7.  Survey of encoding and decoding of visual stimulus via FMRI: an image analysis perspective.

Authors:  Mo Chen; Junwei Han; Xintao Hu; Xi Jiang; Lei Guo; Tianming Liu
Journal:  Brain Imaging Behav       Date:  2014-03       Impact factor: 3.978

8.  Guiding attention of faces through graph based visual saliency (GBVS).

Authors:  Ravi Kant Kumar; Jogendra Garain; Dakshina Ranjan Kisku; Goutam Sanyal
Journal:  Cogn Neurodyn       Date:  2019-01-02       Impact factor: 5.082

9.  Deep Multimodal Fusion Autoencoder for Saliency Prediction of RGB-D Images.

Authors:  Kengda Huang; Wujie Zhou; Meixin Fang
Journal:  Comput Intell Neurosci       Date:  2021-05-05

10.  Automated Detection of Vessel Abnormalities on Fluorescein Angiogram in Malarial Retinopathy.

Authors:  Yitian Zhao; Ian J C MacCormick; David G Parry; Nicholas A V Beare; Simon P Harding; Yalin Zheng
Journal:  Sci Rep       Date:  2015-06-08       Impact factor: 4.379

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