BUBL: An effective region labeling tool using a hexagonal lattice
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BUBL : An effective region labeling tool using a hexagonal lattice. / Galleguillos, Carolina; Faymonville, Peter; Belongie, Serge.
In: 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009, 2009, p. 2072-2079.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - BUBL
T2 - 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009
AU - Galleguillos, Carolina
AU - Faymonville, Peter
AU - Belongie, Serge
PY - 2009
Y1 - 2009
N2 - We propose a data labeling tool that permits accurate labeling of images using less time and effort. Our tool, BUBL, uses a hexagonal grid with a variable size tiling for accurate labeling of object contours. The hexagonal lattice is superimposed by a bubble wrap interface in order to make the labeling task enjoyable. The resulting label mask is represented by a Gaussian kernel density estimator which provides accurate bounding contours, even for objects that include hollow regions. Furthermore, multiple annotations from different users are collected for every image, making it possible to "hint" a partial labeling so the user can finish labeling in less time. We show accuracy results by simulating the application of our labeling tool for the MSRC dataset and to a subset data set of Caltech-101.
AB - We propose a data labeling tool that permits accurate labeling of images using less time and effort. Our tool, BUBL, uses a hexagonal grid with a variable size tiling for accurate labeling of object contours. The hexagonal lattice is superimposed by a bubble wrap interface in order to make the labeling task enjoyable. The resulting label mask is represented by a Gaussian kernel density estimator which provides accurate bounding contours, even for objects that include hollow regions. Furthermore, multiple annotations from different users are collected for every image, making it possible to "hint" a partial labeling so the user can finish labeling in less time. We show accuracy results by simulating the application of our labeling tool for the MSRC dataset and to a subset data set of Caltech-101.
UR - http://www.scopus.com/inward/record.url?scp=77953217348&partnerID=8YFLogxK
U2 - 10.1109/ICCVW.2009.5457536
DO - 10.1109/ICCVW.2009.5457536
M3 - Conference article
AN - SCOPUS:77953217348
SP - 2072
EP - 2079
JO - 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009
JF - 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009
Y2 - 27 September 2009 through 4 October 2009
ER -
ID: 302048797