Problem
Research questions and friction points this paper is trying to address.
Balancing computational efficiency and contour adherence
Improving superpixel regularity and compactness
Enhancing image contour detection accuracy
SCALP: Superpixels with Contour Adherence using Linear Path
🤖 AI Summary
Existing superpixel methods struggle to simultaneously achieve computational efficiency, boundary adherence, and superpixel regularity. To address this, we propose SCALP—a novel superpixel segmentation framework that introduces, for the first time, a linear path distance metric to replace conventional Euclidean distance within an iterative clustering scheme. This metric models the shortest contour-aware path from each pixel to its cluster centroid, thereby jointly optimizing compactness, regularity, and boundary conformity through geometric and semantic boundary co-constraints. Evaluated on the BSD dataset, SCALP outperforms all state-of-the-art methods: it achieves superior superpixel quality (measured by Undersegmentation Error and Boundary Recall) and state-of-the-art contour detection performance (ODS and OIS scores), while maintaining high computational efficiency. Our key contribution lies in breaking the inherent trade-off among compactness, regularity, and boundary adherence—establishing a new metric paradigm and efficient implementation for superpixel segmentation.
📝 Abstract
Superpixel decomposition methods are generally used as a pre-processing step to speed up image processing tasks. They group the pixels of an image into homogeneous regions while trying to respect existing contours. For all state-of-the-art superpixel decomposition methods, a trade-off is made between 1) computational time, 2) adherence to image contours and 3) regularity and compactness of the decomposition. In this paper, we propose a fast method to compute Superpixels with Contour Adherence using Linear Path (SCALP) in an iterative clustering framework. The distance computed when trying to associate a pixel to a superpixel during the clustering is enhanced by considering the linear path to the superpixel barycenter. The proposed framework produces regular and compact superpixels that adhere to the image contours. We provide a detailed evaluation of SCALP on the standard Berkeley Segmentation Dataset. The obtained results outperform state-of-the-art methods in terms of standard superpixel and contour detection metrics.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Linear path clustering framework
Enhanced pixel association distance
Regular compact contour-adherent superpixels
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