In natural images, the scales (thickness) of object skeletons may dramatically vary among objects and object parts, making object skeleton detection a challenging problem. We present a new convolutional neural network (CNN) architecture by introducing a novel hierarchical feature integration mechanism, named Hi-Fi, to address the skeleton detection problem. The proposed CNNbased approach has a powerful multi-scale feature integration ability that intrinsically captures highlevel semantics from deeper layers as well as lowlevel details from shallower layers. By hierarchically integrating different CNN feature levels with bidirectional guidance, our approach (1) enables mutual refinement across features of different levels, and (2) possesses the strong ability to capture both rich object context and high-resolution details. Experimental results show that our method significantly outperforms the state-of-the-art methods in terms of effectively fusing features from very different scales, as evidenced by a considerable performance improvement on several benchmarks. Code is available at http://mmcheng.net/hifi.
In addition, the Hi-Fi method REF presents hierarchical feature integration and bidirectional mutual refinement strategies for skeleton detection, that enhances the ability of capturing enrich features of objects.
Hi-Fi: Hierarchical Feature Integration for Skeleton Detection
ArXiv
ArXiv
Computer Science