Image Segmentation by Cascaded Superpixel Merging with Privileged Information
- Title
- Image Segmentation by Cascaded Superpixel Merging with Privileged Information
- Authors
- 박용진
- Date Issued
- 2019
- Publisher
- 포항공과대학교
- Abstract
- We propose a learning-based image segmentation algorithm. Starting from
superpixels, our method learns the probability of merging two regions based on
the ground truth made by humans. The learned information is used in determining
whether the two regions should be merged or not in a segmentation stage.
Unlike exiting learning-based algorithms, we use both local and object information.
The local information represents features computed from super pixels and
the object information represent high level information available only in the learning
process. The object information is considered as privileged information, and
we can use a framework that utilize the privileged information such as SVM+ and
AdaBoost+. In experiments on the Berkeley Segmentation Dataset and Benchmark(
BSDS500) and PASCAL Visual Object Classes Challenge(VOC 2012) data
set, our model exhibited the best performance with a relatively small training
data set and also showed competitive results with a su ciently large training
data set.
- URI
- http://postech.dcollection.net/common/orgView/200000220738
https://oasis.postech.ac.kr/handle/2014.oak/111354
- Article Type
- Thesis
- Files in This Item:
- There are no files associated with this item.
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