A Baseline Statistical Method for Robust User-Assisted Multiple Segmentation

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Tarih

2022

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

IEEE-Inst Electrical Electronics Engineers Inc

Erişim Hakkı

info:eu-repo/semantics/openAccess

Özet

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 hounding 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.

Açıklama

Anahtar Kelimeler

DGL test, image segmentation, interactive segmentation, multiple instance segmentation, robust hypothesis testing, user-assisted segmentation

Kaynak

Ieee Signal Processing Letters

WoS Q Değeri

Q2

Scopus Q Değeri

Q1

Cilt

29

Sayı

Künye