Scene parsing with deep features and spatial structure learning

Hui Yu, Yuecheng Song, Wenyu Ju, Zhenbao Liu

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

Conditional Random Field (CRF) is a powerful tool for labeling tasks, and has always played a key role in object recognition and semantic segmentation. However, the quality of CRF labeling depends on selected features, which becomes the bottleneck of the accuracy improvement. In this paper, our semantic segmentation problem is calculated in the same way within the framework of Conditional Random Field. Different from other CRF-based strategies, which use appearance features of image, revealing only little information, we combined our framework together with deep learning strategy, such as Convolutional Neural Networks (CNNs), for feature extraction, which have shown strong ability and remarkable performance. This combination strategy is called deepfeature CRF (dCRF). Through dCRF, the deep informantion of image is illustrated and gets ultilized, and the segmentation accuracy is also increased. The proposed deep CRF strategy is adopted on SIFT-Flow and VOC2007 datasets. The segmentation results reveals that if we use features learned from deep networks into our CRF framework, the performance of our semantic segmentation strategy would increase significantly.

源语言英语
主期刊名Advances in Multimedia Information Processing – 17th Pacific-Rim Conference on Multimedia, PCM 2016, Proceedings
编辑Enqing Chen, Yun Tie, Yihong Gong
出版商Springer Verlag
715-722
页数8
ISBN(印刷版)9783319488950
DOI
出版状态已出版 - 2016
活动17th Pacific-Rim Conference on Multimedia, PCM 2016 - Xi’an, 中国
期限: 15 9月 201616 9月 2016

出版系列

姓名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
9917 LNCS
ISSN(印刷版)0302-9743
ISSN(电子版)1611-3349

会议

会议17th Pacific-Rim Conference on Multimedia, PCM 2016
国家/地区中国
Xi’an
时期15/09/1616/09/16

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