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 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.
Highlights
Segmentation is the act of partitioning an image into different regions by creating boundaries that keep regions apart
The same evaluation parameters were used for other selected clustering segmentation algorithms for comparative analysis, but for the deep learning based segmentation algorithm, only intersection over union (IoU) and mean intersection over union (MIoU) were used for comparison with the proposed segmentation algorithm
We presented a convolution-based modified adaptive k-means algorithm, to get the best out of the normal k-means method during image segmentation
Summary
Segmentation is the act of partitioning an image into different regions by creating boundaries that keep regions apart. It is one of the most used steps in zoning pixels of an image [1]. The discontinuous nature of pixels characterizes all algorithms in the edge-based segmentation family [2]. In this type of image segmentation, images are segmented into partitions based on unanticipated changes in gray intensity in the image. Edge-based segmentation techniques can identify corners, edges, points, and lines in the image. The edge detection technique is an example of this class of segmentation method [2]
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