Open AccessSaliency optimization via low rank matrix recovery with multi-prior integration
Author(s) -
Dongjing Shan,
Guibin Zhu,
Chao Zhang
Publication year - 2016
Publication title -
ieee/caa journal of automatica sinica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.277
H-Index - 41
eISSN - 2329-9274
pISSN - 2329-9266
DOI - 10.1109/jas.2016.7510241
Subject(s) - computing and processing , communication, networking and broadcast technologies , general topics for engineers , robotics and control systems
In this paper, we propose an unsupervised saliency detection method based on the low rank matrix recovery model (LRMR). Learning of a feature transform matrix is not needed and three low level priors are integrated in our model in replacing of the original high level ones, which could act as better guidance cues. Also an optimization framework is designed to optimize the raw saliency map generated by the low rank matrix recovery model. We compare our method with seven previous methods and test them on several benchmark datasets. The results demonstrate that our model achieves state of the art performance.