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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: Goh, Kok Luong, Ng, Giap Weng, Muzaffar, Hamzah, Chai, Soo See
Format: Proceeding
Language:English
Published: 2021
Subjects:
QA75 Electronic computers. Computer science
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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spelling my.unimas.ir.366082021-11-05T07:10:06Z http://ir.unimas.my/id/eprint/36608/ Sizes of Superpixels and their Effect on Interactive Segmentation Goh, Kok Luong Ng, Giap Weng Muzaffar, Hamzah Chai, Soo See QA75 Electronic computers. Computer science 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. 2021 Proceeding PeerReviewed text en http://ir.unimas.my/id/eprint/36608/1/Chai%20Soo%20See.pdf Goh, Kok Luong and Ng, Giap Weng and Muzaffar, Hamzah and Chai, Soo See (2021) Sizes of Superpixels and their Effect on Interactive Segmentation. In: 2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), 13-15 Sept. 2021, Kota Kinabalu, Malaysia. https://ieeexplore.ieee.org/document/9573623
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Goh, Kok Luong
Ng, Giap Weng
Muzaffar, Hamzah
Chai, Soo See
Sizes of Superpixels and their Effect on Interactive Segmentation
description 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.
format Proceeding
author Goh, Kok Luong
Ng, Giap Weng
Muzaffar, Hamzah
Chai, Soo See
author_facet Goh, Kok Luong
Ng, Giap Weng
Muzaffar, Hamzah
Chai, Soo See
author_sort Goh, Kok Luong
title Sizes of Superpixels and their Effect on Interactive Segmentation
title_short Sizes of Superpixels and their Effect on Interactive Segmentation
title_full Sizes of Superpixels and their Effect on Interactive Segmentation
title_fullStr Sizes of Superpixels and their Effect on Interactive Segmentation
title_full_unstemmed Sizes of Superpixels and their Effect on Interactive Segmentation
title_sort sizes of superpixels and their effect on interactive segmentation
publishDate 2021
url 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
_version_ 1717097750618701824
score 13.223943

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