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Image Segmentation via Probabilistic Graph Matching.

Ayelet Heimowitz, Yosi Keller

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

|July 15, 2016

Summary

This summary is machine-generated.

This study introduces a novel unsupervised and semi-automatic image segmentation method using probabilistic graph matching. The approach effectively incorporates low-level image cues, outperforming existing unsupervised and semi-supervised techniques.

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Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Image segmentation is crucial for image analysis.
Existing unsupervised and semi-supervised methods have limitations.
Integrating low-level image cues is challenging.

Purpose of the Study:

To develop an unsupervised and semi-automatic image segmentation approach.
To formulate segmentation as an inference problem using probabilistic graph matching.
To enable automatic parameter tuning and rigorous cue incorporation.

Main Methods:

Formulating segmentation as an inference problem.
Computing unary and pairwise assignment probabilities from low-level image cues.
Solving inference via probabilistic graph matching.

Main Results:

The proposed scheme demonstrates favorable comparison with contemporary methods.
Experimental results validate the effectiveness on state-of-the-art image sets.
The method successfully incorporates low-level image cues.

Conclusions:

The probabilistic graph matching scheme offers a robust approach to image segmentation.
The method provides an effective unsupervised and semi-automatic solution.
This technique advances the field of image segmentation.