DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection (1412.1123v3)

Abstract: Contour detection has been a fundamental component in many image segmentation and object detection systems. Most previous work utilizes low-level features such as texture or saliency to detect contours and then use them as cues for a higher-level task such as object detection. However, we claim that recognizing objects and predicting contours are two mutually related tasks. Contrary to traditional approaches, we show that we can invert the commonly established pipeline: instead of detecting contours with low-level cues for a higher-level recognition task, we exploit object-related features as high-level cues for contour detection. We achieve this goal by means of a multi-scale deep network that consists of five convolutional layers and a bifurcated fully-connected sub-network. The section from the input layer to the fifth convolutional layer is fixed and directly lifted from a pre-trained network optimized over a large-scale object classification task. This section of the network is applied to four different scales of the image input. These four parallel and identical streams are then attached to a bifurcated sub-network consisting of two independently-trained branches. One branch learns to predict the contour likelihood (with a classification objective) whereas the other branch is trained to learn the fraction of human labelers agreeing about the contour presence at a given point (with a regression criterion). We show that without any feature engineering our multi-scale deep learning approach achieves state-of-the-art results in contour detection.

• The paper introduces a novel approach by integrating object-specific features for top-down contour detection, challenging traditional low-level methods.
• It employs a bifurcated deep network with parallel streams optimizing classification and regression, significantly boosting contour precision on BSDS500.
• Results demonstrate state-of-the-art performance, setting the stage for future advancements in CNN-based contour and low-level vision tasks.

Overview of the DeepEdge Approach for Contour Detection

The paper "DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection" introduces an innovative approach to contour detection by leveraging high-level object information within a deep learning framework. Traditionally in computer vision, contour detection has been treated as a low-level task employing features like texture and saliency, which precede higher-level tasks such as object detection. This paper diverges from that paradigm by proposing a top-down strategy that utilizes object-specific features to inform the contour detection process.

The Core Architecture

The proposed system comprises a multi-scale bifurcated deep network. Central to this architecture is the reuse of features from the initial layers of a pre-trained network optimized for object classification, specifically the network from Krizhevsky et al. This deep network, which includes five convolutional layers, processes input at multiple image scales. Four parallel streams of the convolutional section input to a bifurcated sub-network, each branch optimizing a different objective: one for contour likelihood prediction using a classification objective and another for predicting the fraction of human label agreement using a regression objective.

Performance and Results

Utilizing this architecture, the paper achieves state-of-the-art contour detection accuracy on the BSDS500 dataset, demonstrating superior average precision and F-score compared to contemporary methods. The system's high precision at low recall levels underscores its efficacy in accurately detecting contours. This performance is attributed in part to the use of object-level features, as the system incorporates information from a network trained on a large-scale object recognition task.

Comparative Analysis

Significant improvements over prior methods such as gPb-owt-ucm and more recent techniques like SCT and DeepNet are noted. While techniques like Structured Edges and $N^4$ fields have also shown commendable effectiveness, the DeepEdge method presents distinct advantages by uniquely harnessing high-level features and optimizing distinct objectives for classification and regression within its network branches.

Implications and Future Directions

The bifurcated network structure, by independently optimizing classification and regression objectives, highlights a nuanced approach to leveraging deep networks for contour detection, resulting in enhanced precision and recall measures. This top-down method not only addresses the task of contour detection with remarkable efficacy but also suggests broader implications for deploying convolutional neural networks (CNNs) across other low-level vision tasks.

Moving forward, further refinements in computational efficiency, perhaps through feature interpolation techniques, could enhance the real-time applicability of DeepEdge. Moreover, integrating this approach with spectral methods or CRFs might enhance global contour coherence, potentially yielding even greater accuracy and practical utility in diverse computer vision applications.

Conclusion

"DeepEdge" marks a notable contribution to contour detection research, coupling high-level object feature integration with a multi-scale deep network for refined edge detection. By fundamentally challenging and inverting traditional methodologies, this paper offers insights and methodologies that may inform advancements in both contour detection and broader vision-based AI tasks.