TY - GEN
T1 - Semi-supervised segmentation fusion of multi-spectral and aerial images
AU - Ozay, Mete
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/12/4
Y1 - 2014/12/4
N2 - A Semi-supervised Segmentation Fusion algorithm is proposed using consensus and distributed learning. The aim of Unsupervised Segmentation Fusion (USF) is to achieve a consensus among different segmentation outputs obtained from different segmentation algorithms by computing an approximate solution to the NP problem with less computational complexity. Semi-supervision is incorporated in USF using a new algorithm called Semi-supervised Segmentation Fusion (SSSF). In SSSF, side information about the co-occurrence of pixels in the same or different segments is formulated as the constraints of a convex optimization problem. The results of the experiments employed on artificial and real-world benchmark multi-spectral and aerial images show that the proposed algorithms perform better than the individual state-of-the art segmentation algorithms.
AB - A Semi-supervised Segmentation Fusion algorithm is proposed using consensus and distributed learning. The aim of Unsupervised Segmentation Fusion (USF) is to achieve a consensus among different segmentation outputs obtained from different segmentation algorithms by computing an approximate solution to the NP problem with less computational complexity. Semi-supervision is incorporated in USF using a new algorithm called Semi-supervised Segmentation Fusion (SSSF). In SSSF, side information about the co-occurrence of pixels in the same or different segments is formulated as the constraints of a convex optimization problem. The results of the experiments employed on artificial and real-world benchmark multi-spectral and aerial images show that the proposed algorithms perform better than the individual state-of-the art segmentation algorithms.
KW - Clustering
KW - Consensus
KW - Fusion
KW - Segmentation
KW - Stochastic optimization
UR - http://www.scopus.com/inward/record.url?scp=84919933973&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84919933973&partnerID=8YFLogxK
U2 - 10.1109/ICPR.2014.659
DO - 10.1109/ICPR.2014.659
M3 - Conference contribution
AN - SCOPUS:84919933973
T3 - Proceedings - International Conference on Pattern Recognition
SP - 3839
EP - 3844
BT - Proceedings - International Conference on Pattern Recognition
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd International Conference on Pattern Recognition, ICPR 2014
Y2 - 24 August 2014 through 28 August 2014
ER -