HERS Superpixels: Deep Affinity Learning for Hierarchical Entropy Rate Segmentation
This work addresses the speed bottleneck in superpixel methods for computer vision applications, enabling more practical use in real-time tasks.
The authors tackled the problem of slow superpixel generation by proposing a two-stage framework combining deep affinity learning with hierarchical entropy rate segmentation, achieving near real-time performance while maintaining competitive segmentation quality.
Superpixels serve as a powerful preprocessing tool in numerous computer vision tasks. By using superpixel representation, the number of image primitives can be largely reduced by orders of magnitudes. With the rise of deep learning in recent years, a few works have attempted to feed deeply learned features / graphs into existing classical superpixel techniques. However, none of them are able to produce superpixels in near real-time, which is crucial to the applicability of superpixels in practice. In this work, we propose a two-stage graph-based framework for superpixel segmentation. In the first stage, we introduce an efficient Deep Affinity Learning (DAL) network that learns pairwise pixel affinities by aggregating multi-scale information. In the second stage, we propose a highly efficient superpixel method called Hierarchical Entropy Rate Segmentation (HERS). Using the learned affinities from the first stage, HERS builds a hierarchical tree structure that can produce any number of highly adaptive superpixels instantaneously. We demonstrate, through visual and numerical experiments, the effectiveness and efficiency of our method compared to various state-of-the-art superpixel methods.