Fast Edge Detection Using Structured Forests (1406.5549v2)

Abstract: Edge detection is a critical component of many vision systems, including object detectors and image segmentation algorithms. Patches of edges exhibit well-known forms of local structure, such as straight lines or T-junctions. In this paper we take advantage of the structure present in local image patches to learn both an accurate and computationally efficient edge detector. We formulate the problem of predicting local edge masks in a structured learning framework applied to random decision forests. Our novel approach to learning decision trees robustly maps the structured labels to a discrete space on which standard information gain measures may be evaluated. The result is an approach that obtains realtime performance that is orders of magnitude faster than many competing state-of-the-art approaches, while also achieving state-of-the-art edge detection results on the BSDS500 Segmentation dataset and NYU Depth dataset. Finally, we show the potential of our approach as a general purpose edge detector by showing our learned edge models generalize well across datasets.

• The paper introduces a structured learning framework that predicts segmentation masks for enhanced edge detection performance.
• It leverages random decision forests to capture local edge structures, improving accuracy by accounting for inherent image patterns.
• Enhancements such as multiscale detection and edge sharpening yield state-of-the-art performance with real-time processing capabilities.

Fast Edge Detection Using Structured Forests: An Expert Overview

The paper "Fast Edge Detection Using Structured Forests" by Piotr Dollár and C. Lawrence Zitnick presents a novel approach for enhancing edge detection tasks in computer vision. By leveraging the local structure in image patches, the authors introduce a structured learning framework for edge detection implemented using random decision forests, which achieves both high accuracy and computational efficiency.

Structured Learning Framework for Edge Detection

Edge detection is a foundational task in computer vision, integral to processes like object recognition and image segmentation. Traditional methods often face limitations with texture edges and illusory contours. Recent learning-based approaches predict edges in image patches and then combine these predictions. However, these methods usually treat each pixel independently, potentially losing local edge structure.

Dollár and Zitnick propose a method that directly learns the structured local patterns of edges using decision forests. The key innovation is formulating edge detection as predicting segmentation masks from local image patches within a structured learning framework. This approach exploits the inherent structure of edge patches, represented as straight lines, T-junctions, or other patterns seen in edges.

Random Decision Forests with Structured Labels

The method adapts the traditional decision tree to handle structured output spaces. Each tree in the forest is trained to predict a structured label—local segmentation masks—using a binary split function optimized for information gain. Unlike typical random forests, which predict a single label, structured forests predict a patch of edge labels aggregated into a final edge map.

The splitting criterion is enhanced by mapping structured labels (segmentation masks) to a discrete space where information gain can be calculated efficiently. This is achieved through a two-stage mapping: first to an intermediate space where Euclidean distances are computed, and then to a discrete domain for effective split decisions.

Enhancements and Performance

Two enhancements—Multiscale Detection (SE+MS) and Edge Sharpening (SE+SH)—are proposed to improve the baseline Structured Edge (SE) detector:

1. Multiscale Detection (SE+MS): This uses multiple resolutions of the input image, enhancing precision by averaging edge maps from different scales.
2. Edge Sharpening (SE+SH): This locally aligns predicted segmentations with the color and depth channels, refining the edge map.

These enhancements result in notable performance improvements. The SE+SH variant, particularly, demonstrates high recall, critical for applications demanding exhaustive edge detection.

Numerical Results and Implications

The developed edge detector attained state-of-the-art results on standard benchmarks like BSDS500 and NYU Depth (NYUD). On BSDS500, SE+MS+SH achieved an optimal dataset scale (ODS) of 0.75, surpassing competing methods. The method also generalizes well across datasets, with a slight drop in performance when trained on one dataset and tested on another.

The speed of the SE detector, running at 30 frames per second (fps) for real-time processing, sets it apart in practical applications. The introduction of edge sharpening and multiscale processing increases computation slightly (SE+SH at 12.5 fps), yet remains faster than most existing high-accuracy edge detectors.

Practical and Theoretical Implications

Practically, this approach facilitates real-time edge detection in application areas such as autonomous driving, robotic vision, and interactive image editing. The theoretical contribution lies in the structured learning framework's adaptability to other computer vision problems where local patterns are critical, such as semantic segmentation or object part detection.

Looking ahead, the structured learning framework could be extended to more complex tasks in AI, leveraging its efficient prediction and generalization capabilities. Enhancing the robustness and scalability of structured forests could propel advancements in dynamic visual systems and real-time AI applications.

Conclusion

The structured forests approach for edge detection provides a compelling balance of accuracy and efficiency, showcasing the potential of structured learning in computer vision. The innovations in handling structured outputs and the practical enhancements ensure that this method remains relevant and adaptable for future developments in AI.