NDLI: Adaptive appearance separation for interactive image segmentation based on Dense CRF

Content Provider	IET Digital Library
Author	Peng, Zili Li, Qiaoliang
Abstract	Interactive segmentation has recently become a hot topic for its wide application. The authors propose an efficacious appearance separation model for interactive binary segmentation, which incorporates the difference of foreground and background colour models and the difference of corresponding geodesic models into the popular densely connected conditional random field (Dense CRF) framework. The proposed method can adaptively set relevant parameter values in this framework according to the characteristics of target images in a per-image manner, therefore, it gets rid of the dependence on specific datasets. After accomplishing a mean-field inference, the authors are able to get satisfactory results without the time-consuming parameter learning process and multiple iterative optimisations. Overall, the proposed approach is highly efficient and mitigates the contradiction between accuracy and segmentation efficiency. In addition, the proposed approach reduces the efforts of scribble-style interaction from users. The experimental results on three famous datasets show that the proposed method is superior to the other five new algorithms released in recent years regarding accuracy, and is faster than or close to them in runtime.
Starting Page	142
Ending Page	151
Page Count	10
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 1, Jan (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5073
Journal	IET Image Processing
Publisher Date	2018-10-25
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Appearance Separation Background Colour Model Computer Vision And Image Processing Technique Corresponding Geodesic Model Dense CRF Efficacious Appearance Separation Model Foreground Hot Topic Image Colour Analysis Image Segmentation Interactive Binary Segmentation Interactive Image Segmentation Interactive Segmentation Learning in AI Mean-field Inference Optical, Image And Video Signal Processing Per-image Manner Popular Densely Connected Conditional Random Field Framework Relevant Parameter Value Scribble-style Interaction Segmentation Efficiency Target Image Time-consuming Parameter Wide Application
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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