Abstract
In natural images, the scales (thickness) of object skeletons may
dramatically vary among objects and object parts.
Thus, robust skeleton detection requires powerful multi-scale feature integration ability.
To address this issue, we present a new convolutional neural network (CNN) architecture
by introducing a novel hierarchical feature integration mechanism,
named Hi-Fi, to address the object skeleton detection problem.
The proposed CNN-based approach 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.
Keywords
- Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation
- Computer Vision: Computer Vision
