Sizes of Superpixels and their Effect on Interactive Segmentation
Semi-automated segmentation, also known as interactive image segmentation, is an algorithm that extracts a region of interest (ROI) from an image based on user input. The said algorithm will be fed the user input information repeatedly until the required region of interest is successfully segme...
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| Main Authors: | , , , |
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| Format: | Proceeding |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/36608/1/Chai%20Soo%20See.pdf http://ir.unimas.my/id/eprint/36608/ https://ieeexplore.ieee.org/document/9573623 |
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| Summary: | Semi-automated segmentation, also known as
interactive image segmentation, is an algorithm that extracts
a region of interest (ROI) from an image based on user input.
The said algorithm will be fed the user input information
repeatedly until the required region of interest is successfully
segmented. Pre-processing steps can be used to speed up the
segmentation process while improving the end result. The use
of superpixels is one example of such pre-processing step. A
superpixel is a group of pixels that share similar
characteristics such as texture and colour. Despite the fact
that it is used as a pre-processing step in many interactive
segmentation algorithms, less studies had been conducted to
assess the effects of the size of superpixels required by
interactive segmentation algorithms to achieve an optimal
result. Therefore, the purpose of this research is to address
this issue in order to bridge this research gap. This study will
be performed using the Maximum Similarity based region
merging (MSRM) with input strokes on selected images from
the Berkeleys and Grabcut image data sets, generated by
superpixels extractions via energy-driven samples (SEEDS
We infer from this research that an image with a minimum of
500 superpixels will aid the interactive segmentation
algorithm in producing a decent segmentation result with
pixel accuracy of 0.963, F-score of 0.844, and Jaccard index of
0.756. When the superpixels for an image are raised to 10,000,
the segmentation results degrade. In conclusion, the size of the
superpixels would have an impact on the final segmentation
results. |
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