Aarhus University Seal

Muhammad Rizwan Asif

Salient region detection through salient and non-salient dictionaries

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

  • Mian Muhammad Sadiq Fareed, Xi'an Jiaotong University
  • ,
  • Qi Chun, Xi'an Jiaotong University
  • ,
  • Gulnaz Ahmed, Xi'an Jiaotong University
  • ,
  • Adil Murtaza, Xi'an Jiaotong University
  • ,
  • Muhammad Rizwan Asif
  • Muhammad Zeeshan Fareed, Xi'an Jiaotong University

Low-rank representation-based frameworks are becoming popular for the saliency and the object detection because of their easiness and simplicity. These frameworks only need global features to extract the salient objects while the local features are compromised. To deal with this issue, we regularize the low-rank representation through a local graph-regularization and a maximum mean-discrepancy regularization terms. Firstly, we introduce a novel feature space that is extracted by combining the four feature spaces like CIELab, RGB, HOG and LBP. Secondly, we combine a boundary metric, a candidate objectness metric and a candidate distance metric to compute the low-level saliency map. Thirdly, we extract salient and non-salient dictionaries from the low-level saliency. Finally, we regularize the low-rank representation through the Laplacian regularization term that saves the structural and geometrical features and using the mean discrepancy term that reduces the distribution divergence and connections among similar regions. The proposed model is tested against seven latest salient region detection methods using the precision-recall curve, receiver operating characteristics curve, F-measure and mean absolute error. The proposed model remains persistent in all the tests and outperformed against the selected models with higher precision value.

Original languageEnglish
Article numbere0213433
Publication statusPublished - Mar 2019
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported in part by the National Natural Science Foundation of China (Grant no. 60972124), in part by the National High-tech Research and Development Program of China (Grant no. 2009AA01Z321) and in part of the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant no. 20110201110012). There was no additional external funding received for this study.

Publisher Copyright:
© 2019 Fareed et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

See relations at Aarhus University Citationformats

ID: 299982019