03^{rd}FEBRUARY2018

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.

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:

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.

Manifold SLIC: a fast method to compute content-sensitive superpixels. CVPR, 2016.SLIC superpixels ompared to state-of-the-art superpixel methods. PAMI, 2012.