Region-adaptive superpixel segmentation algorithm based on density clustering

Mingyang Shao; Houfei Liu

doi:10.1117/12.3029810

21 June 2024 Region-adaptive superpixel segmentation algorithm based on density clustering

Mingyang Shao, Houfei Liu

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 1316724 (2024) https://doi.org/10.1117/12.3029810
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

In addressing the issue of unstable boundary adherence in traditional superpixel segmentation algorithms, this paper proposes a novel region-adaptive superpixel segmentation algorithm based on density clustering for computer vision research. Firstly, during the initialization of superpixels, a region-adaptive initialization method is employed based on the complexity of image content. This method takes into consideration the diversity of image content, thereby reducing redundancy in the initialization to a certain extent. Secondly, within the initialized superpixel regions, two pixels properties, namely distance and membership relationship, are computed. Pixels with higher local density are chosen as seed points, and their membership relationships are marked. Finally, seed points closest to the pixels are selected in the membership relationships, and labels are assigned to the pixels, completing the superpixel segmentation of the image. Experimental results, compared with six advanced algorithms, demonstrate that the proposed algorithm exhibits superior performance in terms of boundary recall rate, under-segmentation error rate, and accuracy while maintaining a higher level of edge adherence.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mingyang Shao and Houfei Liu "Region-adaptive superpixel segmentation algorithm based on density clustering", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 1316724 (21 June 2024); https://doi.org/10.1117/12.3029810

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KEYWORDS

Image segmentation

Image processing algorithms and systems

Image processing

Visualization

Algorithms

Detection and tracking algorithms

RGB color model

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