Adaptive tuning of SLIC parameter K

The well-known simple linear iterative clustering (SLIC) is the most effective among the existing algorithms for superpixel segmentation, which requires manual tuning of the number of superpixels K . The optimal value of the parameter K of the SLIC algorithm for a given image is yet an open issue. I...

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Bibliographic Details
Published in:Multimedia tools and applications 2021-07, Vol.80 (17), p.25649-25672
Main Authors: Ullah, Shakir, Bhatti, Naeem, Zia, Muhammad
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Datasets
Image segmentation
Methods
Multimedia
Multimedia Information Systems
Parameters
Special Purpose and Application-Based Systems
Tuning
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Summary:The well-known simple linear iterative clustering (SLIC) is the most effective among the existing algorithms for superpixel segmentation, which requires manual tuning of the number of superpixels K . The optimal value of the parameter K of the SLIC algorithm for a given image is yet an open issue. In this work, we present granulometry and quality metrics based methods for adaptive tuning of the parameter K . The proposed granulometric method exploits the weighted average of the image pattern spectrum for the adaptive tuning of the parameter K . In the quality metrics method, we use majority voting scheme based on information, texture and ground truth independent quality metrics. The experimental results demonstrate that the K SLIC superpixels from the proposed methods achieved good boundary adherence of the ground truth for the images with high value of the compactness.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-021-10900-5