Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform (1511.03328v2)

Abstract: Deep convolutional neural networks (CNNs) are the backbone of state-of-art semantic image segmentation systems. Recent work has shown that complementing CNNs with fully-connected conditional random fields (CRFs) can significantly enhance their object localization accuracy, yet dense CRF inference is computationally expensive. We propose replacing the fully-connected CRF with domain transform (DT), a modern edge-preserving filtering method in which the amount of smoothing is controlled by a reference edge map. Domain transform filtering is several times faster than dense CRF inference and we show that it yields comparable semantic segmentation results, accurately capturing object boundaries. Importantly, our formulation allows learning the reference edge map from intermediate CNN features instead of using the image gradient magnitude as in standard DT filtering. This produces task-specific edges in an end-to-end trainable system optimizing the target semantic segmentation quality.

• The paper introduces a unified CNN-based system that integrates EdgeNet with a domain transform to refine segmentation maps efficiently.
• It leverages intermediate CNN features for task-specific edge learning, reducing computational overhead compared to dense CRFs.
• Empirical results on datasets like PASCAL VOC 2012 demonstrate competitive mIOU scores and robust edge detection performance.

Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform

The paper addresses the challenge of improving the localization accuracy of semantic image segmentation systems by proposing a novel approach that combines deep convolutional neural networks (CNNs) with a discriminatively trained domain transform (DT) for edge-preserving filtering. Unlike fully-connected conditional random fields (CRFs), which have been traditionally used to refine segmentation maps at the cost of high computational overhead, the proposed method utilizes a DT, which is significantly faster and capable of achieving comparable results.

The proposed methodology comprises three components: the DeepLab model for coarse semantic segmentation score predictions, the EdgeNet for predicting task-specific edge maps, and the DT for refining the segmentation scores based on the edge maps. The authors confirm that the EdgeNet utilizes intermediate CNN features to learn task-specific edge detection, which is optimized end-to-end to improve semantic segmentation quality.

A key innovation is the integration of CNNs with the DT for a unified end-to-end trainable system. Unlike previous methods where edge detection and segmentation were treated as separate tasks, this approach facilitates task-specific edge learning with DT. Moreover, the DT framework lends itself to being recast as a recurrent neural network, particularly similar to a gated recurrent unit (GRU), enabling the leverage of insights from recurrent network methodologies.

The empirical evaluation on the PASCAL VOC 2012 dataset demonstrates the efficacy of the approach, showing competitive mean intersection-over-union (mIOU) scores with significantly reduced computational cost compared to dense CRFs. Furthermore, the learned object-specific edges achieved competitive performance on the BSDS500 edge detection benchmark, underscoring the robustness of the learned edges.

In terms of broader implications, this research introduces a viable alternative to dense CRFs for enhancing CNN-derived segmentation maps with edge-preserving refinement, offering practitioners an efficient and scalable solution. This approach may pave the way for a new line of investigation into end-to-end trainable systems that harness intermediate CNN features for specialized tasks, such as edge detection tailored to specific semantic labels.

Future applications could foreseeably extend this framework to other domains where computational efficiency and task-specific edge detection are paramount, such as autonomous driving, medical imaging, and real-time systems, where both speed and precision are critical. Furthermore, this work may stimulate further research into the symbiotic design of CNNs with other non-linear filters to optimize for various computer vision tasks.

Overall, this paper makes a substantial contribution to the field of semantic segmentation by offering a refined methodology that improves segmentation accuracy with a real-world feasible computational profile, providing a foundation for future research and implementation in practical domains.