TY - GEN
T1 - Saliency detection via combining global shape and local cue estimation
AU - Qi, Qiang
AU - Jian, Muwei
AU - Yin, Yikang
AU - Dong, Junyu
AU - Zhang, Wenyin
AU - Yu, Hui
PY - 2017/9/14
Y1 - 2017/9/14
N2 - Recently, saliency detection has become a hot issue in computer vision. In this paper, a novel framework for image saliency detection is introduced by modeling global shape and local cue estimation simultaneously. Firstly, Quaternionic Distance Based Weber Descriptor (QDWD), which was initially designed for detecting outliers in color images, is used to model the salient object shape in an image. Secondly, we detect local saliency based on the reconstruction error by using a locality-constrained linear coding algorithm. Finally, by integrating global shape with local cue, a reliable saliency map can be computed and estimated. Experimental results, based on two widely used and openly available databases, show that the proposed method can produce reliable and promising results, compared to other state-of-the-art saliency detection algorithms.
AB - Recently, saliency detection has become a hot issue in computer vision. In this paper, a novel framework for image saliency detection is introduced by modeling global shape and local cue estimation simultaneously. Firstly, Quaternionic Distance Based Weber Descriptor (QDWD), which was initially designed for detecting outliers in color images, is used to model the salient object shape in an image. Secondly, we detect local saliency based on the reconstruction error by using a locality-constrained linear coding algorithm. Finally, by integrating global shape with local cue, a reliable saliency map can be computed and estimated. Experimental results, based on two widely used and openly available databases, show that the proposed method can produce reliable and promising results, compared to other state-of-the-art saliency detection algorithms.
KW - saliency detection
KW - QDWD
KW - locality-constrained linear coding
KW - local cue
U2 - 10.1007/978-3-319-67777-4_28
DO - 10.1007/978-3-319-67777-4_28
M3 - Conference contribution
SN - 978-3319677767
T3 - Lecture Notes in Computer Science
SP - 325
EP - 334
BT - Intelligence Science and Big Data Engineering
A2 - Sun, Y.
A2 - Zhang, L.
A2 - Yang, J.
A2 - Huang, H.
PB - Springer
T2 - IScIDE 2017: International Conference on Intelligence Science and Big Data Engineering
Y2 - 23 September 2017 through 24 September 2017
ER -