TY - GEN
T1 - Image segmentation with adaptive sparse grids
AU - Peherstorfer, Benjamin
AU - Adorf, Julius
AU - Pflüger, Dirk
AU - Bungartz, Hans Joachim
PY - 2013
Y1 - 2013
N2 - We present a novel adaptive sparse grid method for unsupervised image segmentation. The method is based on spectral clustering. The use of adaptive sparse grids achieves that the dimensions of the involved eigensystem do not depend on the number of pixels. In contrast to classical spectral clustering, our sparse-grid variant is therefore able to segment larger images. We evaluate the method on real-world images from the Berkeley Segmentation Dataset. The results indicate that images with 150,000 pixels can be segmented by solving an eigenvalue system of dimensions 500 x 500 instead of 150,000 x 150,000.
AB - We present a novel adaptive sparse grid method for unsupervised image segmentation. The method is based on spectral clustering. The use of adaptive sparse grids achieves that the dimensions of the involved eigensystem do not depend on the number of pixels. In contrast to classical spectral clustering, our sparse-grid variant is therefore able to segment larger images. We evaluate the method on real-world images from the Berkeley Segmentation Dataset. The results indicate that images with 150,000 pixels can be segmented by solving an eigenvalue system of dimensions 500 x 500 instead of 150,000 x 150,000.
KW - Image segmentation
KW - Out-of-sample extension
KW - Sparse grids
UR - http://www.scopus.com/inward/record.url?scp=84893749999&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-03680-9_17
DO - 10.1007/978-3-319-03680-9_17
M3 - Conference contribution
AN - SCOPUS:84893749999
SN - 9783319036793
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 160
EP - 165
BT - AI 2013
T2 - 26th Australasian Joint Conference on Artificial Intelligence, AI 2013
Y2 - 1 December 2013 through 6 December 2013
ER -