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Multiple-region segmentation without supervision by adaptive global maximum clustering.

Sunhee Kim¹, Myungjoo Kang

¹Department of Mathematical Sciences, Seoul National University, Seoul, Korea. sunny068@snu.ac.kr

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

|December 15, 2011

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This study introduces a novel image segmentation method that automatically determines the optimal number of regions. The technique effectively segments both clean and noisy images, improving accuracy and efficiency.

Area of Science:

Computer Vision
Image Processing
Pattern Recognition

Background:

Image segmentation is crucial for analyzing visual data.
Determining the number of regions (segmentation) is often unstable and depends on prior knowledge.
Existing methods struggle with accurately identifying the number of regions in noisy images.

Purpose of the Study:

To develop a robust image segmentation method.
To automatically determine the optimal number of distinct regions in an image.
To enhance segmentation performance for both clean and noisy images.

Main Methods:

Proposed a two-procedure method for image segmentation.
Developed adaptive global maximum clustering using image histograms to identify significant local maxima.

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Derived a fast calculation for segmenting images into multiple distinct regions based on histogram analysis.

Main Results:

The method successfully identifies a reasonable number of distinct regions in images.
Demonstrated effectiveness in segmenting both clean and noisy images.
Outperformed previous methods in efficiency and accuracy.

Conclusions:

The proposed method offers an effective solution for automatic image segmentation.
Adaptive global maximum clustering provides a reliable way to determine the number of image regions.
The technique is efficient and applicable to various image conditions, including noise.