Fast image segmentation using region merging with a k-Nearest Neighbor graph

Liu, Hongzhi; Guo, Qiyong; Xu, Mantao; Shen, I-Fan

doi:10.1109/iccis.2008.4670856

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…The weakness is this method depends on the Lambda constant [30]. Some methods combine the classical approach with the neural network, such as combining the region-based method with k-NN [38]. Table 3 demonstrate the characteristic of each color space with the benefit and handicap, which then opened up a greater possibility of segmentation technique by utilizing the character of the spaces.…”

Section: Figure 3 Course Of the Studymentioning

confidence: 99%

“…They perform image enhancement followed by the dilation residue method to find the edges [45]. Liu et al in 2008 utilized the fast region imaging method, with the concept that all pixels with similar features should be segmented in the same region [46]. They focus on the relationship between nearby pixels instead of the whole image feature.…”

Section: Figure 3 Course Of the Studymentioning

confidence: 99%

See 1 more Smart Citation

Image Segmentation in Aerial Imagery: A Review

Purwanto,

Budiarti,

Purnamastuti

et al. 2023

Sinergi

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The problem of distinguishing objects has plagued researchers for many years because of low accuracy compared to human eyes’ capability. In the last decade, the use of Machine Learning in aerial imagery data processing has multiplied, with the technology behind it has also developed exponentially. One of those technologies is image-based object identification, which relies heavily upon data computation. To reduce the computational load, various data segmentation algorithm was developed. This study is focused on reviewing the various image segmentation technology in aerial imagery for image recognition. Literature from as far as 1981 from various journals and conferences worldwide was reviewed. This review examines specific research questions to analyze image segmentation research over time and the challenges researchers face with each method. Machine Learning has gained popularity among segmentation methods. However, Deep Learning has been aggressively put an essential role in it by overcoming many of its weaknesses. The advanced algorithm used in Deep Learning to process the segmentation may drive more efficient and accurate data processing.

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Section: Figure 3 Course Of the Studymentioning

confidence: 99%

Section: Figure 3 Course Of the Studymentioning

confidence: 99%

Image Segmentation in Aerial Imagery: A Review

Purwanto,

Budiarti,

Purnamastuti

et al. 2023

Sinergi

View full text Add to dashboard Cite

show abstract

Efficient and accurate semi-supervised semantic segmentation for industrial surface defects

Shi,

Wang,

Zhang

et al. 2024

Sci Rep

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A robust multi-agent Negotiation for advanced Image Segmentation: Design and Implementation

Allioui

Sadgal

Fazziki

2019

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It is generally accepted that segmentation is a critical problem that influences subsequent tasks during image processing. Often, the proposed approaches provide effectiveness for a limited type of images with a significant lack of a global solution. The difficulty of segmentation lies in the complexity of providing a global solution with acceptable accuracy within a reasonable time. To overcome this problem, some solutions combined several methods. This paper presents a method for segmenting 2D/3D images by merging regions and solving problems encountered during the process using a multi-agent system (MAS). We are using the strengths of MAS by opting for a compromise that satisfies segmentation by agents’ acts. Regions with high similarity are merged immediately, while the others with low similarity are ignored. The remaining ones, with ambiguous similarity, are solved in a coalition by negotiation. In our system, the agents make decisions according to the utility functions adopting the Pareto optimal in Game theory. Unlike hierarchical merging methods, MAS performs a hypothetical merger planning then negotiates the agreements' subsets to merge all regions at once.

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Fast image segmentation using region merging with a k-Nearest Neighbor graph

Cited by 3 publications

References 17 publications

Image Segmentation in Aerial Imagery: A Review

Image Segmentation in Aerial Imagery: A Review

Efficient and accurate semi-supervised semantic segmentation for industrial surface defects

A robust multi-agent Negotiation for advanced Image Segmentation: Design and Implementation

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