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Improving SLIC superpixel by color difference-based region merging

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Abstract

Superpixel-based segmentation has been widely used as a primary prepossessing step to simplify the subsequent image processing tasks. Since determining the number of clusters is subjective and varies based on the type of image, the segmentation algorithm may provide over-segmented or under-segmented superpixels. This paper proposes an image segmentation method to improve the SLIC superpixel by region merging. It aims to improve the segmentation accuracy without defining a precise number of superls. The color difference between superpixels is employed as a homogeneity criterion for the merging process. The Berkeley dataset is used with different quantitative performance metrics to evaluate the proposed model’s performance. Results obtained from probabilistic rand index (PRI), boundary recall, and under-segmentation error proved the ability of the proposed algorithm to provide comparable segmentation with a reduced number of clusters.

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Fig. 1
Algorithm 1
Algorithm 2

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Data availability

The data generated and analysed during the current study are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. Faculty of Engineering and Information Technology, Arab American University, Jenin, Palestine

    Kefaya Sabaneh & Muath Sabha

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  1. Kefaya Sabaneh
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  2. Muath Sabha
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Correspondence to Kefaya Sabaneh.

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Sabaneh, K., Sabha, M. Improving SLIC superpixel by color difference-based region merging. Multimed Tools Appl 83, 47943–47961 (2024). https://doi.org/10.1007/s11042-023-17304-7

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