Dynamic Spectral Residual Superpixels
This is an incremental improvement for computer vision applications requiring precise image segmentation.
The paper tackles image segmentation into superpixels by enhancing SLIC with a spectral residual measure to preserve object boundaries and uniform areas, resulting in improved performance over state-of-the-art methods.
We consider the problem of segmenting an image into superpixels in the context of $k$-means clustering, in which we wish to decompose an image into local, homogeneous regions corresponding to the underlying objects. Our novel approach builds upon the widely used Simple Linear Iterative Clustering (SLIC), and incorporate a measure of objects' structure based on the spectral residual of an image. Based on this combination, we propose a modified initialisation scheme and search metric, which helps keeps fine-details. This combination leads to better adherence to object boundaries, while preventing unnecessary segmentation of large, uniform areas, while remaining computationally tractable in comparison to other methods. We demonstrate through numerical and visual experiments that our approach outperforms the state-of-the-art techniques.