TY - JOUR
T1 - Semi-supervised salient object detection using a linear feedback control system model
AU - Zhou, Yuan
AU - Huo, Shuwei
AU - Xiang, Wei
AU - Hou, Chunping
AU - Kung, Sun Yuan
N1 - Funding Information:
Manuscript received January 3, 2017; revised July 30, 2017; accepted December 29, 2017. Date of publication April 10, 2018; date of current version February 22, 2019. This work was supported in part by the National Natural Science Foundation of China under Grant 61571326, Grant 61471262, and Grant 61520106002, and in part by the National Natural Science Foundation of Tianjin under Grant 16JCQNJC00900. This paper was recommended by Associate Editor H. Lu. (Corresponding authors: Shuwei Huo; Wei Xiang.) Y. Zhou is with the School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China, and also with the Department of Electrical Engineering, Princeton University, Princeton, NJ 08540 USA (e-mail: zhouyuan@tju.edu.cn).
Publisher Copyright:
© 2018 IEEE
PY - 2019/4
Y1 - 2019/4
N2 - To overcome the challenging problems in saliency detection, we propose a novel semi-supervised classifier which makes good use of a linear feedback control system (LFCS) model by establishing a relationship between control states and salient object detection. First, we develop a boundary homogeneity model to estimate the initial saliency and background likelihoods, which are regarded as the labeled samples in our semi-supervised learning procedure. Then in order to allocate an optimized saliency value to each superpixel, we present an iterative semi-supervised learning framework which integrates multiple saliency cues and image features using an LFCS model. Via an innovative iteration method, the system gradually converges an optimized stable state, which is associating with an accurate saliency map. This paper also covers comprehensive simulation study based on public datasets, which demonstrates the superiority of the proposed approach.
AB - To overcome the challenging problems in saliency detection, we propose a novel semi-supervised classifier which makes good use of a linear feedback control system (LFCS) model by establishing a relationship between control states and salient object detection. First, we develop a boundary homogeneity model to estimate the initial saliency and background likelihoods, which are regarded as the labeled samples in our semi-supervised learning procedure. Then in order to allocate an optimized saliency value to each superpixel, we present an iterative semi-supervised learning framework which integrates multiple saliency cues and image features using an LFCS model. Via an innovative iteration method, the system gradually converges an optimized stable state, which is associating with an accurate saliency map. This paper also covers comprehensive simulation study based on public datasets, which demonstrates the superiority of the proposed approach.
KW - Linear control system
KW - Saliency detection
KW - Semi-supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85045310532&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045310532&partnerID=8YFLogxK
U2 - 10.1109/TCYB.2018.2793278
DO - 10.1109/TCYB.2018.2793278
M3 - Article
C2 - 29993850
AN - SCOPUS:85045310532
SN - 2168-2267
VL - 49
SP - 1173
EP - 1185
JO - IEEE Transactions on Cybernetics
JF - IEEE Transactions on Cybernetics
IS - 4
M1 - 8334814
ER -