期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2018
卷号：6
期号：12
页码：9197-9202
DOI：10.15680/IJIRCCE.2018. 0612022
出版社：S&S Publications
摘要：Semantic segmentation is the problem of assigning an object label to each pixel. It unifies the image
segmentation and object recognition problems. The importance of using contextual information in semantic
segmentation frameworks has been widely realized in the field. We propose a contextual framework, called contextual
hierarchical model (CHM), which learns contextual information in a hierarchical framework for semantic segmentation.
At each level of the hierarchy, a classifier is trained based on down sampled input images and outputs of previous
levels. Our model then incorporates the resulting mustier solution contextual information into a classifier to segment
the input image at original resolution. This training strategy allows for optimization of a joint posterior probability at
multiple resolutions through the hierarchy. Contextual hierarchical model is purely based on the input image patches
and does not make use of any fragments or shape examples. Hence, it is applicable to a variety of problems such as
object segmentation and edge detection. We demonstrate that CHM outperforms state-of-the-art on Stanford
background and Weizmann horse datasets. It also outperforms state-of-the-art edge detection methods on NYU depth
dataset and achieves state-of-the-art on Berkeley segmentation dataset.
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