Abstract
Superpixels could effectively decompose an image into perceptually meaningful partitions, thus facilitating various high-level computer vision tasks. As a pre-processing tool to speed up the subsequent steps, superpixel generation is expected to be outstanding in decomposition quality and computational efficiency. In this work, we introduce the contour prior into a non-iterative clustering framework and then put forward a package of optimizations. First, contour existence between two pixels on the image plane is utilized as a scaling factor to magnify their joint color and spatial similarity. Then the inter-pixel correlation and the boundaries between different objects can be precisely described. In addition, we conduct an early assignment strategy on the conventional non-iterative clustering structure based on the smooth assumption, leading to efficient label expansion during updating and assignment. Moreover, we adopt a contour-tuned redistribution strategy to generate content-aware superpixels with the exact amount to substitute the grid sampling. Finally, these optimizations could form a synergistic framework to generate higher-quality superpixels. Extensive experiments confirm that the proposed method outperforms the baselines and achieves comparable results with state-of-the-art superpixel algorithms for several quantitative metrics.
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Acknowledgements
This research is supported financially by the National Natural Science Foundation of China (62171341, 51805398), Natural Science Basic Research Program of Shaanxi Province of China (2020JM-196) and Fundamental Research Funds for the Central Universities (JB211303).
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Li, C., He, W., Liao, N. et al. Superpixels with contour adherence via label expansion for image decomposition. Neural Comput & Applic 34, 16223–16237 (2022). https://doi.org/10.1007/s00521-022-07315-0
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DOI: https://doi.org/10.1007/s00521-022-07315-0