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2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014)
Columbus, OH, USA
June 23, 2014 to June 28, 2014
ISSN: 1063-6919
ISBN: 978-1-4799-5118-5
pp: 2417-2424
We present a method for generating object segmentation proposals from groups of superpixels. The goal is to propose accurate segmentations for all objects of an image. The proposed object hypotheses can be used as input to object detection systems and thereby improve efficiency by replacing exhaustive search. The segmentations are generated in a class-independent manner and therefore the computational cost of the approach is independent of the number of object classes. Our approach combines both global and local search in the space of sets of superpixels. The local search is implemented by greedily merging adjacent pairs of superpixels to build a bottom-up segmentation hierarchy. The regions from such a hierarchy directly provide a part of our region proposals. The global search provides the other part by performing a set of graph cut segmentations on a superpixel graph obtained from an intermediate level of the hierarchy. The parameters of the graph cut problems are learnt in such a manner that they provide complementary sets of regions. Experiments with Pascal VOC images show that we reach state-of-the-art with greatly reduced computational cost.
Proposals, Image segmentation, Search problems, Merging, Histograms, Object segmentation, Image color analysis

P. Rantalankila, J. Kannala and E. Rahtu, "Generating Object Segmentation Proposals Using Global and Local Search," 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA, 2014, pp. 2417-2424.
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