Fixation Prediction in Videos using Unsupervised Hierarchical Features

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

4 Citations (Scopus)


This paper presents a framework for saliency estimation and fixation prediction in videos. The proposed framework is based on a hierarchical feature representation obtained by stacking convolutional layers of independent subspace analysis (ISA) filters. The feature learning is thus unsupervised and independent of the task. To compute the saliency, we then employ a multiresolution saliency architecture that exploits both local and global saliency. That is, for a given image, an image pyramid is initially built. After that, for each resolution, both local and global saliency measures are computed to obtain a saliency map. The integration of saliency maps over the image pyramid provides the final video saliency. We first show that combining local and global saliency improves the results. We then compare the proposed model with several video saliency models and demonstrate that the proposed framework is capable of predicting video saliency effectively, outperforming all the other models.
Original languageEnglish
Title of host publicationProceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017
Number of pages8
ISBN (Electronic)9781538607336
Publication statusPublished - 22 Aug 2017
MoE publication typeA4 Article in a conference publication
EventIEEE Conference on Computer Vision and Pattern Recognition - Hawaii Convention Center, Honolulu, United States
Duration: 21 Jul 201726 Jul 2017
Conference number: 30

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops
ISSN (Electronic)2160-7516


ConferenceIEEE Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVPR
CountryUnited States

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