Abstract
Using superpixels instead of pixels has become a popular pre-processing step in computer vision. However, there are few adaptive methods able to automatically find the best comprise between boundary adherence and superpixel number. Moreover, no algorithm producing color and texture homogeneous superpixels keeps competitive execution time. In this article we suggest a new graph-based region merging method, called Adaptive Superpixel Algorithm with Rich Information (ASARI) to solve these two difficulties. We will show that ASARI achieves results similar to the state-of-the-art methods on the existing benchmarks and outperforms these methods when dealing with big images.
The work of Bérengère Mathieu was partially supported by ANR-11-LABX-0040-CIMI within the program ANR-11-IDEX-0002-02.
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The decision to merge or not \(v_{i}\) and its most similar neighbor is related to the merging criterion.
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Our experiments show that this number of iterations is sufficient to provide good results.
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Mathieu, B., Crouzil, A., Puel, J.B. (2017). ASARI: A New Adaptive Oversegmentation Method. In: Alexandre, L., Salvador Sánchez, J., Rodrigues, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2017. Lecture Notes in Computer Science(), vol 10255. Springer, Cham. https://doi.org/10.1007/978-3-319-58838-4_22
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