The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
A Randomized Framework for Estimating Image Saliency Through Sparse Signal Reconstruction
|
|
Author(s): Kui Fu (State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China)and Jia Li (State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, International Research Institute for Multidisciplinary Science, Beihang University, Beijing, China)
Copyright: 2018
Volume: 9
Issue: 2
Pages: 20
Source title:
International Journal of Multimedia Data Engineering and Management (IJMDEM)
Editor(s)-in-Chief: Chengcui Zhang (University of Alabama at Birmingham, USA)and Shu-Ching Chen (University of Missouri-Kansas City, United States)
DOI: 10.4018/IJMDEM.2018040101
Purchase
|
Abstract
This article proposes a randomized framework that estimates image saliency through sparse signal reconstruction. The authors simulate the measuring process of ground-truth saliency and assume that an image is free-viewed by several subjects. In the free-viewing process, each subject attends to a limited number of regions randomly selected, and a mental map of the image is reconstructed by using the subject-specific prior knowledge. By assuming that a region is difficult to be reconstructed will become conspicuous, the authors represent the prior knowledge of a subject by a dictionary of sparse bases pre-trained on random images and estimate the conspicuity score of a region according to the activation costs of sparse bases as well as the sparse reconstruction error. Finally, the saliency map of an image is generated by summing up all conspicuity maps obtained. Experimental results show proposed approach achieves impressive performance in comparisons with 16 state-of-the-art approaches.
Related Content
|
Yasasi Abeysinghe, Bhanuka Mahanama, Gavindya Jayawardena, Yasith Jayawardana, Mohan Sunkara, Andrew T. Duchowski, Vikas Ashok, Sampath Jayarathna.
© 2024.
20 pages.
|
|
Chengxuan Huang, Evan Brock, Dalei Wu, Yu Liang.
© 2023.
23 pages.
|
|
Duleep Rathgamage Don, Jonathan Boardman, Sudhashree Sayenju, Ramazan Aygun, Yifan Zhang, Bill Franks, Sereres Johnston, George Lee, Dan Sullivan, Girish Modgil.
© 2023.
17 pages.
|
|
Wei-An Teng, Su-Ling Yeh, Homer H. Chen.
© 2023.
17 pages.
|
|
Hemanth Gudaparthi, Prudhviraj Naidu, Nan Niu.
© 2022.
20 pages.
|
|
Anchen Sun, Yudong Tao, Mei-Ling Shyu, Angela Blizzard, William Andrew Rothenberg, Dainelys Garcia, Jason F. Jent.
© 2022.
19 pages.
|
|
Suvojit Acharjee, Sheli Sinha Chaudhuri.
© 2022.
16 pages.
|
|
|