Evaluation of modified adaptive k-means segmentation algorithm

Evaluation of modified adaptive k-means segmentation algorithm

Blog Article

Abstract 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 Dash Parts and Interior Accessories from different sources (MATLAB, Berkeley image database, VOC2012, Hockey Protective - Shoulder Pads - Youth 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.