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03rdFEBRUARY2018

READING

Y. J. Liu, M. Yu, B. J. Li and Y. He. Intrinsic Manifold SLIC: A Simple and Efficient Method for Computing Content-Sensitive Superpixels. PAMI, 2017.

Liu et al. propose intrinsic manifold SLIC (IMSLIC) based on their earlier work [1]. While the authors introduce the theoretical background of performing SLIC on a 2-manifold in $\mathbb{R}^5$ (i.e. coordiantes + color), the overall algorithm is very similar to the original SLIC [2] algorithm. Essentially, stretching factors $\lambda_1$ and $\lambda_2$ are used to define the map

$\Psi(p, c) = (\lambda_1 p, \lambda_2 c)$

where $p$ is the spatial position and $c$ the color of a pixel. Then, SLIC is applied using a geodesic distance instead of Eucliean distance. Additionally, the local search area around each seed is augmented using the stretch factors – which are computed per seed. In contrast to SLIC, IMSLIC additionally uses random initialization on the 2-manifold. Overall, the proposed algorithm produces qualitatively reasonable superpixels, see Figure 2, and is shown to outperform SLIC – in total, the authors compare 11 superpixel algorthms. The proposed method has several advantages over the original SLIC algorithm: it generates exactly the desired number of superpixels, the initialization adapts automatically to the image content and it performs slightly better. However, I also want to note that the superpixels – in practice, i.e. Figure 1 – look very similar.

Figure 1: Qualitative results in comparison to other superpixel algorithms.

  • [1] Y.-J. Liu, C. Yu, M. Yu, Y. He. Manifold SLIC: a fast method to compute content-sensitive superpixels. CVPR, 2016.
  • [2] R. Achanta, A. Sahji, K. Smith, A. Lucchi, P. Fua and S. Süsstrunk. SLIC superpixels ompared to state-of-the-art superpixel methods. PAMI, 2012.

What is your opinion on the summarized work? Or do you know related work that is of interest? Let me know your thoughts in the comments below or get in touch with me: