Abstract
In this paper, we propose a superpixel segmentation method which utilizes extracted deep features along with the combination of color and position information of the pixels. It is observed that the results can be improved significantly using better initial seed points. Therefore, we incorporated a one-step k-means clustering to calculate the positions of the initial seed points and applied the active search method to ensure that each pixel belongs to the right seed. The proposed method was also compared to other state-of-the-art methods quantitatively and qualitatively, and was found to produce promising results that adhere to the object boundaries better than others.
This work was supported by National Natural Science Foundation of China under Grants 61602338, Hubei Foundation for Innovative Research Groups under Grants 2015CFA025.
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Awaisu, M., Li, L., Peng, J., Zhang, J. (2019). Fast Superpixel Segmentation with Deep Features. In: Gavrilova, M., Chang, J., Thalmann, N., Hitzer, E., Ishikawa, H. (eds) Advances in Computer Graphics. CGI 2019. Lecture Notes in Computer Science(), vol 11542. Springer, Cham. https://doi.org/10.1007/978-3-030-22514-8_38
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DOI: https://doi.org/10.1007/978-3-030-22514-8_38
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