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An Extensive Survey on Superpixel Segmentation: A Research Perspective

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Abstract

Several Superpixel segmentation techniques have recently been developed. Superpixels combine perceptually similar pixels to form visually significant entities, lowering the amount of primitives required for future processing stages. We give a thorough examination of several superpixel algorithms, including watershed-based, graph-based, clustering-based, and energy optimization strategies. Along with the various computational intelligence techniques that are currently used by the researchers in the domain of Machine learning, Evolutionary computing and Deep Learning also looked at benchmark measures including accuracy, recall, intra-cluster variation, mean distance to edge, under segmentation error, and sum-of-squared error, as well as datasets like BSD500, SBD, NYUV2, SUNRGBD, FLASH, and PASCAL-S, to create a superpixel benchmark and its applications. This study will insight into the various superpixel segmentation techniques and also will assist researchers in determining the suitable superpixel segmentation approaches that are more feasible for their various problems in carrying out the research in this field.

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  1. Department of Computer Science & Engineering, PSG College of Technology, Coimbatore, India

    Prakash J

  2. Department of Information Technology, PSG College of Technology, Coimbatore, India

    B. Vinoth Kumar

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J, P., Kumar, B.V. An Extensive Survey on Superpixel Segmentation: A Research Perspective. Arch Computat Methods Eng 30, 3749–3767 (2023). https://doi.org/10.1007/s11831-023-09919-8

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