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.
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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.