Holistically-Nested Edge Detection (1504.06375v2)

Abstract: We develop a new edge detection algorithm that tackles two important issues in this long-standing vision problem: (1) holistic image training and prediction; and (2) multi-scale and multi-level feature learning. Our proposed method, holistically-nested edge detection (HED), performs image-to-image prediction by means of a deep learning model that leverages fully convolutional neural networks and deeply-supervised nets. HED automatically learns rich hierarchical representations (guided by deep supervision on side responses) that are important in order to approach the human ability resolve the challenging ambiguity in edge and object boundary detection. We significantly advance the state-of-the-art on the BSD500 dataset (ODS F-score of .782) and the NYU Depth dataset (ODS F-score of .746), and do so with an improved speed (0.4 second per image) that is orders of magnitude faster than some recent CNN-based edge detection algorithms.

• The paper introduces a novel algorithm that applies fully convolutional networks and deep supervision to generate edge maps directly from images.
• It employs nested multi-scale feature learning to progressively refine predictions, improving both accuracy and computational efficiency.
• Experiments on BSD500 and NYUD datasets demonstrate superior performance with an ODS F-score of 0.782 and real-time processing at 0.4 seconds per image.

Holistically-Nested Edge Detection: An Overview

The paper "Holistically-Nested Edge Detection," authored by Saining Xie and Zhuowen Tu, introduces a novel edge detection algorithm known as Holistically-Nested Edge Detection (HED). The proposed method aims at addressing two pivotal issues in edge detection: holistic image training/prediction and multi-scale/multi-level feature learning. Utilizing a deep learning framework, specifically fully convolutional neural networks (FCNs) combined with deeply-supervised nets (DSNs), HED demonstrates substantial improvements in both accuracy and computational efficiency over prior state-of-the-art techniques.

Key Contributions

1. Holistic Image Training and Prediction: HED employs an image-to-image prediction approach derived from FCNs, enabling the model to directly output edge maps from input images without the need for patch-based processing. This significantly enhances both training and prediction efficiency.
2. Nested Multi-Scale Feature Learning: Inspired by deeply-supervised nets, HED introduces deep supervision at multiple layers of the network. This deep supervision facilitates the learning of rich hierarchical features crucial for resolving ambiguities in edge and object boundary detection. The nested architecture allows for progressively refined predictions across different scales, producing edge maps that become increasingly concise.

Numerical Results and Performance

The HED algorithm was rigorously evaluated on two benchmark datasets: BSD500 and NYU Depth (NYUD) dataset. On BSD500, HED achieved an Optimal Dataset Scale (ODS) F-score of .782 and an ODS F-score of .746 on the NYUD dataset. Notably, HED also demonstrated a significant speed advantage, processing images at approximately .4 seconds per image, which is orders of magnitude faster than existing CNN-based methods.

Implications and Future Work

The research presents notable theoretical and practical implications:

• Theoretical Implications: The integration of deep supervision within the HED framework presents a compelling paradigm for training deep networks to perform edge detection. The nested architecture not only facilitates hierarchical feature learning but also provides a pathway for future exploration in structured prediction tasks beyond edge detection.
• Practical Implications: The significant speed and accuracy improvements offered by HED make it highly relevant for real-time applications such as autonomous driving, mobile computing, and medical imaging. The ability to generate high-quality edge maps swiftly can enhance object detection, recognition, and scene understanding in these contexts.

Future Developments

Future research can build on the HED framework to explore several avenues:

• Incorporating Contextual Information: Currently, HED does not explicitly incorporate contextual information in its predictions. Future work could investigate methods for integrating both short- and long-range contextual cues to further enhance edge detection performance.
• Expanding Training Data: The paper shows potential gains when additional training data is used. Creating larger, more diverse datasets specific to edge detection could push the performance of HED even closer to human benchmarks.
• Applications in 3D and Multispectral Imaging: Extending the HED framework to handle 3D data or multispectral images could open new possibilities in fields like remote sensing, medical imaging, and environmental monitoring.

Conclusion

The Holistically-Nested Edge Detection algorithm represents a significant advancement in edge detection, offering a robust, efficient, and accurate approach. The dual focus on holistic image training and nested multi-scale feature learning provides a versatile framework that promises further development and application across multiple domains in computer vision. The open-source nature of HED's implementation encourages continued innovation and adaptation within the research community. The code and models are available at https://github.com/s9xie/hed, facilitating widespread adoption and collaborative improvement.