Paper Info
Title
Multi-scale deep context convolutional neural networks for semantic segmentation
Abstract
Recent years have witnessed the great progress for semantic segmentation using deep convolutional neural networks (DCNNs). This paper presents a novel fully convolutional network for semantic segmentation using multi-scale contextual convolutional features. Since objects in natural images tend to be with various scales and aspect ratios, capturing the rich contextual information is very critical for dense pixel prediction. On the other hand, when going deeper in convolutional layers, the convolutional feature maps of traditional DCNNs gradually become coarser, which may be harmful for semantic segmentation. According to these observations, we attempt to design a multi-scale deep context convolutional network (MDCCNet), which combines the feature maps from different levels of network in a holistic manner for semantic segmentation. The segmentation outputs of MDCCNets are further enhanced using dense connected conditional random fields (CRF). The proposed network allows us to fully exploit local and global contextual information, ranging from an entire scene to every single pixel, to perform pixel-wise label estimation. The experimental results demonstrate that our method outperforms or is comparable to state-of-the-art methods on PASCAL VOC 2012 and SIFTFlow semantic segmentation datasets.
Year
DOI
Venue
2019
10.1007/s11280-018-0556-3
World Wide Web
Keywords
Field
DocType
Multi-scale context, MDCNNs, Semantic segmentation, CRF
Conditional random field,Contextual information,Segmentation,Convolutional neural network,Computer science,Exploit,Ranging,Pixel,Artificial intelligence,Machine learning
Journal
Volume
Issue
ISSN
22
SP2
1573-1413
Citations 
PageRank 
References 
7
0.47
42
Authors
9
Name
Order
Citations
PageRank
Quan Zhou1565.31
Quan Zhou2565.31
Wenbing Yang3140.95
Wenbing Yang4140.95
Guangwei Gao5884.18
Weihua Ou615417.40
Huimin Lu778073.60
Jie Chen89138.15
Longin Jan Latecki93301176.88