A Baseline Statistical Method For Robust User-Assisted Multiple
Segmentation
- URL: http://arxiv.org/abs/2201.02779v1
- Date: Sat, 8 Jan 2022 06:55:45 GMT
- Title: A Baseline Statistical Method For Robust User-Assisted Multiple
Segmentation
- Authors: Huseyin Afser
- Abstract summary: We propose a simple yet effective, statistical segmentation method that can handle and utilize different input types and amounts.
The proposed method is based on robust hypothesis testing, specifically the DGL test, and can be implemented with time complexity that is linear in the number of pixels and quadratic in the number of image regions.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Recently, several image segmentation methods that welcome and leverage
different types of user assistance have been developed. In these methods, the
user inputs can be provided by drawing bounding boxes over image objects,
drawing scribbles or planting seeds that help to differentiate between image
boundaries or by interactively refining the missegmented image regions. Due to
the variety in the types and the amounts of these inputs, relative assessment
of different segmentation methods becomes difficult. As a possible solution, we
propose a simple yet effective, statistical segmentation method that can handle
and utilize different input types and amounts. The proposed method is based on
robust hypothesis testing, specifically the DGL test, and can be implemented
with time complexity that is linear in the number of pixels and quadratic in
the number of image regions. Therefore, it is suitable to be used as a baseline
method for quick benchmarking and assessing the relative performance
improvements of different types of user-assisted segmentation algorithms. We
provide a mathematical analysis on the operation of the proposed method,
discuss its capabilities and limitations, provide design guidelines and present
simulations that validate its operation.
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