Evaluation of modified adaptive k-means segmentation algorithm
| Autor: | Friedhelm Schwenker, Samuel Rahimeto, Taye Girma Debelee, Dereje Yohannes |
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| Rok vydání: | 2019 |
| Předmět: |
Mean squared error
Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology Fuzzy logic lcsh:QA75.5-76.95 Convolution Artificial Intelligence Histogram 0202 electrical engineering electronic engineering information engineering Segmentation MATLAB computer.programming_language modified adaptive k-means (MAKM) segmentation k-means clustering 020207 software engineering Image segmentation Q-value Computer Graphics and Computer-Aided Design 020201 artificial intelligence & image processing lcsh:Electronic computers. Computer science Computer Vision and Pattern Recognition computer Algorithm clustering |
| Zdroj: | Computational Visual Media, Vol 5, Iss 4, Pp 347-361 (2019) |
| ISSN: | 2096-0662 2096-0433 |
| DOI: | 10.1007/s41095-019-0151-2 |
| Popis: | Segmentation is the act of partitioning an image into different regions by creating boundaries between regions. k-means image segmentation is the simplest prevalent approach. However, the segmentation quality is contingent on the initial parameters (the cluster centers and their number). In this paper, a convolution-based modified adaptive k-means (MAKM) approach is proposed and evaluated using images collected from different sources (MATLAB, Berkeley image database, VOC2012, BGH, MIAS, and MRI). The evaluation shows that the proposed algorithm is superior to k-means++, fuzzy c-means, histogram-based k-means, and subtractive k-means algorithms in terms of image segmentation quality (Q-value), computational cost, and RMSE. The proposed algorithm was also compared to state-of-the-art learning-based methods in terms of IoU and MIoU; it achieved a higher MIoU value. |
| Databáze: | OpenAIRE |
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