A Comparative Study of Interactive Segmentation with Different Number of Strokes on Complex Images

Goh, Kok Luong; Ng, Giap Weng; Hamzah, Muzaffar; Chai, Soo See

doi:10.18517/ijaseit.10.1.10240

Cited by 4 publications

(2 citation statements)

References 20 publications

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“… Previous studies [49,50] has found that image complexity can affect segmentation results. Existing interactive image segmentation algorithms have not addressed the relationship between image complexity and input type, as well as superpixel and size, and this can be investigated further.…”

Section: Conclusion and Future Directionmentioning

confidence: 97%

Superpixel Sizes using Topology Preserved Regular Superpixel Algorithm and their Impacts on Interactive Segmentation

Goh¹,

Chai²,

Ng³

et al. 2022

IJACSA

Self Cite

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Interactive Image Segmentation is a type of semiautomated segmentation that uses user input to extract the object of interest. It is possible to speed up and improve the end result of segmentation by using pre-processing steps. The use of superpixels is an example of a pre-processing step. A superpixel is a collection of pixels with similar properties such as texture and colour. Previous research was conducted to assess the impact of the number of superpixels (based on SEEDS superpixel aglorithms) required to achieve the best segmentation results. The study, however, only examined one type of input (strokes) and a small number of images. As a result, the goal of this study is to extend previous work by performing interactive segmentation with input strokes and a combination of bounding box and strokes on images from Grabcut image data sets generated by Topology preserved regular superpixel (TPRS). Based on our findings, an image with 1000 to 2500 superpixels and a combination of bounding box and strokes will help the interactive segmentation algorithm produce a good segmentation result. Finally, the size of the superpixels would influence the final segmentation results as well as the input type.

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Section: Conclusion and Future Directionmentioning

confidence: 97%

Superpixel Sizes using Topology Preserved Regular Superpixel Algorithm and their Impacts on Interactive Segmentation

Goh¹,

Chai²,

Ng³

et al. 2022

IJACSA

Self Cite

View full text Add to dashboard Cite

show abstract

“…These include images with similar foreground and background colours, images with complex contents, and images with noise [4]. Additionally, a recent study introduced a new category of complex images, wherein the object of interest is overlapped with other similar objects [5].…”

Section: Introductionmentioning

confidence: 99%

How Does Image Complexity Affect the Accuracy of an Interactive Image Segmentation Algorithm?

Kok Luong Goh,

Soo See Chai,

Muzaffar Hamzah

et al. 2024

ARASET

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This study investigates the impact of image complexity on the accuracy of interactive image segmentation algorithms. Image complexity plays a crucial role in segmentation performance, yet previous studies have primarily relied on subjective methods, leaving a gap in understanding how objective measures impact accuracy. The purpose of this research is to explore the relationship between image complexity and segmentation performance and to propose an adaptive approach for improving accuracy based on complexity measures. The study utilizes objective measures, namely entropy and fractal dimension, to quantify image complexity. An interactive image segmentation algorithm is employed, with a bounding box as the background and strokes as the foreground annotations. The number of strokes is dynamically adjusted based on complexity measures, ensuring a tailored segmentation approach. Comparative evaluations are conducted to assess the effectiveness of dynamic and fixed stroke allocation strategies. The principal results reveal a significant influence of image complexity on segmentation accuracy. The dynamic stroke allocation strategy outperforms fixed allocation, highlighting the importance of adapting to complexity. Moreover, the optimal combination of strokes and superpixel sizes is explored, providing valuable insights for practitioners. The findings emphasize the need to consider image complexity in segmentation algorithms to achieve accurate results. In conclusion, this study contributes to the understanding of the relationship between image complexity and interactive image segmentation. By introducing a dynamic stroke allocation approach and evaluating different configurations, the research provides insights into optimizing accuracy based on image complexity. The adaptive strategy improves segmentation performance and guides the development of robust algorithms. Future research can further refine the adaptive approach, explore additional complexity measures, and incorporate advanced machine learning techniques to enhance interactive image segmentation. Overall, this study advances the field by highlighting the importance of image complexity, providing guidance for practitioners, and paving the way for more efficient segmentation algorithms.

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Sizes of Superpixels and their Effect on Interactive Segmentation

Goh

Hamzah

et al. 2021

2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)

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A Comparative Study of Interactive Segmentation with Different Number of Strokes on Complex Images

Cited by 4 publications

References 20 publications

Superpixel Sizes using Topology Preserved Regular Superpixel Algorithm and their Impacts on Interactive Segmentation

Superpixel Sizes using Topology Preserved Regular Superpixel Algorithm and their Impacts on Interactive Segmentation

How Does Image Complexity Affect the Accuracy of an Interactive Image Segmentation Algorithm?

Sizes of Superpixels and their Effect on Interactive Segmentation

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