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An Iterative Spanning Forest Framework for Superpixel Segmentation.

John E Vargas-Munoz, Ananda S Chowdhury, Eduardo B Alexandre

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 9, 2019

Summary

This summary is machine-generated.

The Iterative Spanning Forest (ISF) framework generates improved superpixels using image foresting transforms. ISF methods offer flexible component choices and achieve top performance in image segmentation tasks.

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Area of Science:

Computer Vision
Image Processing

Background:

Superpixel segmentation is a critical problem in computer vision and image processing.
Existing methods face challenges in generating effective and efficient superpixel sets.

Purpose of the Study:

To introduce a novel framework, Iterative Spanning Forest (ISF), for generating improved superpixels.
To provide a flexible framework allowing customization of components for diverse applications.

Main Methods:

The ISF framework utilizes a sequence of image foresting transforms.
Key components include seed sampling, connectivity functions, adjacency relations, and seed pixel recomputation.
Five ISF-based methods were developed by varying these components.

Main Results:

ISF-based methods were compared against state-of-the-art approaches on various 2D and 3D datasets.
Performance was evaluated based on effectiveness and efficiency.
Experiments included challenging applications like sky image segmentation.

Conclusions:

ISF-based methods consistently ranked among the best performers across all tested datasets.
The framework demonstrates theoretical properties and practical effectiveness for superpixel generation.