CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
LEARNABLE CONTEXTUAL REGULARIZATION FOR SEMANTIC SEGMENTATION OF INDOOR SCENE IMAGES
Jun Chu1; Xu Xiao2,3; Gaofeng Meng3; Lingfeng Wang3; Chunhong Pan3
2017
Conference NameIEEE International Conference on Image Processing
Conference Date2017-9-17
Conference PlaceBeijing, CHINA
AbstractSemantic segmentation of indoor scene images has a wide range of applications. However, due to a large number of classes and uneven distribution in indoor scenes, mislabels are often made when facing small objects or boundary regions. Technically, contextual information may benefit for segmentation results, but has not yet been exploited sufficiently. In this paper, we propose a learnable contextual regularization model for enhancing the semantic segmentation results of color indoor scene images. This regularization model is combined with a deep convolutional segmentation network without significantly increasing the number of additional parameters. Our model, derived from the inherent contextual regularization on the indoor scene objects, benefits much from the learnable constraint layers bridging the lower layers and the higher layers in the deep convolutional network. The constraint layers are further integrated with a weighted L1-norm based contextual regularization between the neighboring pixels of RGB values to improve the segmentation results. Experimental results on NYUDv2 indoor scene dataset demonstrate the effectiveness and efficiency of the proposed method.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20353
Collection模式识别国家重点实验室_先进数据分析与学习
Affiliation1.Institute of Computer Vision, Nanchang Hangkong University
2.School of Software, Nanchang Hangkong University
3.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Jun Chu,Xu Xiao,Gaofeng Meng,et al. LEARNABLE CONTEXTUAL REGULARIZATION FOR SEMANTIC SEGMENTATION OF INDOOR SCENE IMAGES[C],2017.
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