TY - GEN
T1 - Scene parsing with deep features and spatial structure learning
AU - Yu, Hui
AU - Song, Yuecheng
AU - Ju, Wenyu
AU - Liu, Zhenbao
N1 - Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Conditional random fields (CRFs)
KW - Convolutional neural networks (CNNs)
KW - Deep feature CRF
KW - Deep learning
KW - Scene parsing
UR - http://www.scopus.com/inward/record.url?scp=85006900519&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-48896-7_71
DO - 10.1007/978-3-319-48896-7_71
M3 - 会议稿件
AN - SCOPUS:85006900519
SN - 9783319488950
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 715
EP - 722
BT - Advances in Multimedia Information Processing – 17th Pacific-Rim Conference on Multimedia, PCM 2016, Proceedings
A2 - Chen, Enqing
A2 - Tie, Yun
A2 - Gong, Yihong
PB - Springer Verlag
T2 - 17th Pacific-Rim Conference on Multimedia, PCM 2016
Y2 - 15 September 2016 through 16 September 2016
ER -