🤖 AI Summary
To address the content-dependent nature of superpixel segmentation—which leads to irregular regions and poor matching robustness—this paper proposes SuperPatch, a stable region descriptor based on superpixel neighborhoods, and extends PatchMatch to SuperPatchMatch, incorporating spatial neighborhood structure to enhance cross-image region correspondence accuracy. Furthermore, we develop an end-to-end, database-driven fast annotation framework that integrates superpixel segmentation, randomized matching optimization, neighborhood graph modeling, and similarity-guided cross-image label propagation. Evaluated on facial landmark annotation and medical image segmentation tasks, our method achieves superior accuracy over contemporary state-of-the-art approaches while significantly reducing computational overhead.
📝 Abstract
Superpixels have become very popular in many computer vision applications. Nevertheless, they remain underexploited, since the superpixel decomposition may produce irregular and nonstable segmentation results due to the dependency to the image content. In this paper, we first introduce a novel structure, a superpixel-based patch, called SuperPatch. The proposed structure, based on superpixel neighborhood, leads to a robust descriptor, since spatial information is naturally included. The generalization of the PatchMatch method to SuperPatches, named SuperPatchMatch, is introduced. Finally, we propose a framework to perform fast segmentation and labeling from an image database, and demonstrate the potential of our approach, since we outperform, in terms of computational cost and accuracy, the results of state-of-the-art methods on both face labeling and medical image segmentation.
