NDLI: Edge gradient feature and long distance dependency for image semantic segmentation

Content Provider	IET Digital Library
Author	Zhou, Hao Han, Anqi Yang, Haodong Zhang, Jun
Abstract	Image semantic segmentation is a challenging problem for low-level computer vision. Recently, deep convolutional neural networks (DCNNs) have been proved to achieve outstanding performance in image semantic segmentation. Most current methods still have some problems in segmenting the object edges and the integrity of objects. In this study, the authors first construct the difference-pooling module in the DCNNs to extract the object edge gradients and get finer boundary in segmentation results. Then the combination of the pyramid pooling module and the atrous spatial pyramid pooling extracts the image global features and the context structure information by building long-distance dependency between pixels, which is just like a simple fully connected conditional random field (CRF). Different from other methods, the proposed method does not need extra pre-processing and post-processing steps, such as extracting gradient features by the traditional algorithm and building context relationships by CRF. Finally, the experimental results on the PASCAL VOC2012 benchmark indicate that the proposed model can obtain the finer boundaries and more complete parts.
Starting Page	53
Ending Page	60
Page Count	8
ISSN	17519632
Volume Number	13
e-ISSN	17519640
Issue Number	Issue 1, Feb (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/13/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2018.5035
Journal	IET Computer Vision
Publisher Date	2018-09-06
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Atrous Spatial Pyramid Pooling Computer Vision Computer Vision And Image Processing Technique Deep Convolutional Neural Network Difference-pooling Module Edge Gradient Feature Extracting Gradient Feature Feature Extraction Image Classification Image Global Feature Image Recognition Image Representation Image Segmentation Image Semantic Segmentation Learning in AI Long Distance Dependency Long-distance Dependency Low-level Computer Vision Neural Nets Object Detection Object Edge Object Edge Gradients Object Recognition Optical, Image And Video Signal Processing Pyramid Pooling Module Segmentation Result
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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