Hi-Fi: Hierarchical Feature Integration for Skeleton Detection

Abstract:

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 CNN-based approach has a powerful multi-scale feature integration ability that intrinsically captures high-level semantics from deeper layers as well as low-level 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.

Architecture:

Performance Evaluation:

We test the proposed method on 4 skeleton datasets in terms of F-measure and pr-curve. The datasets are SK-SMALL, SK-LARGE SYM-PASCAL, and WH-SYMMAX.

Code, Data, and Pretrained Models:

The code will be available in https://github.com/zeakey/skeleton.

Square objects and detection results on SYM-PASCAL dataset