NDLI: Adaptive convolutional neural network for large change in video object segmentation

Content Provider	IET Digital Library
Author	Yin, Hui Yang, Lin Xu, Hongli Wan, Jin
Abstract	This study tackles the semi-supervised segmentation task for the objects that have large motion or appearance change in a video sequence, which is very challenging to the existing methods of video object segmentation (VOS). In this study, a novel adaptive approach is presented, named adaptive convolutional neural network for large change VOS, which determines when and how to fine-tune the convolutional neural network through the motion metric and the appearance metric among consecutive video frames. Additionally, a lightweight optimisation algorithm for the predictive binary mask is introduced which is effective for pixel prediction by eliminating the discrete points cluster. To illustrate the advantages of this approach, experiments have been performed on four VOS datasets, which demonstrate that the proposed method is highly effective and could achieve the state-of-the-art on these datasets.
Starting Page	452
Ending Page	460
Page Count	9
ISSN	17519632
Volume Number	13
e-ISSN	17519640
Issue Number	Issue 5, Aug (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/13/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2018.5387
Journal	IET Computer Vision
Publisher Date	2019-02-25
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Approach Adaptive Convolutional Neural Network Appearance Metric Computer Vision And Image Processing Technique Consecutive Video Frames Convolutional Neural Nets Discrete Points Cluster Image Segmentation Image Sequence Lightweight Optimisation Algorithm Motion Metric Neural Computing Technique Optical, Image And Video Signal Processing Optimisation Optimisation Technique Pixel Prediction Predictive Binary Mask Semisupervised Segmentation Task Video Object Segmentation Video Sequence Video Signal Processing VOS
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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