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A fully convolutional two-stream fusion network for interactive image segmentation.

Yang Hu1, Andrea Soltoggio1, Russell Lock1

  • 1Loughborough University, UK.

Neural Networks : the Official Journal of the International Neural Network Society
|November 4, 2018
PubMed
Summary
This summary is machine-generated.

We introduce a new fully convolutional two-stream fusion network (FCTSFN) for interactive image segmentation. This novel approach enhances user interaction

Keywords:
Fully convolutional networkInteractive image segmentationTwo-stream network

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Interactive image segmentation requires efficient integration of user input with image data.
  • Existing methods often struggle to directly leverage the rich information provided by user interactions.

Purpose of the Study:

  • To propose a novel Fully Convolutional Two-Stream Fusion Network (FCTSFN) for improved interactive image segmentation.
  • To enhance the direct impact of user interactions on segmentation outcomes.

Main Methods:

  • Developed a two-stream late fusion network (TSLFN) to predict foreground at reduced resolution, prioritizing user interaction features.
  • Incorporated a multi-scale refining network (MSRN) to refine segmentation at full resolution by fusing features from different TSLFN layers.
  • Designed TSLFN with two distinct streams to minimize layers between user interaction features and network output.

Main Results:

  • The FCTSFN demonstrated competitive performance against state-of-the-art interactive image segmentation methods.
  • Comprehensive experiments were conducted on four benchmark datasets, validating the network's effectiveness.
  • The two-stream architecture effectively utilized user interactions for more direct influence on segmentation.

Conclusions:

  • The proposed FCTSFN offers a significant advancement in interactive image segmentation.
  • The network architecture successfully balances user interaction guidance with image feature refinement.
  • This method provides a robust solution for accurate and efficient interactive image segmentation tasks.