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논문 기본 정보
- 자료유형
- 학위논문
- 저자정보
- 지도교수
- Minsu Cho
- 발행연도
- 2019
- 저작권
- 과학기술연합대학원대학교 논문은 저작권에 의해 보호받습니다.
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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 suciently large training
data set.
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 suciently large training
data set.
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