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Superpixel Segmentation with Fully Convolutional Networks (2003.12929v1)

Published 29 Mar 2020 in cs.CV

Abstract: In computer vision, superpixels have been widely used as an effective way to reduce the number of image primitives for subsequent processing. But only a few attempts have been made to incorporate them into deep neural networks. One main reason is that the standard convolution operation is defined on regular grids and becomes inefficient when applied to superpixels. Inspired by an initialization strategy commonly adopted by traditional superpixel algorithms, we present a novel method that employs a simple fully convolutional network to predict superpixels on a regular image grid. Experimental results on benchmark datasets show that our method achieves state-of-the-art superpixel segmentation performance while running at about 50fps. Based on the predicted superpixels, we further develop a downsampling/upsampling scheme for deep networks with the goal of generating high-resolution outputs for dense prediction tasks. Specifically, we modify a popular network architecture for stereo matching to simultaneously predict superpixels and disparities. We show that improved disparity estimation accuracy can be obtained on public datasets.

Citations (163)

Summary

  • The paper introduces an FCN method for superpixel segmentation, efficiently integrating it into deep networks by predicting pixel-superpixel associations on image grids.
  • Experimental results show the FCN achieves state-of-the-art on benchmark datasets, exhibiting superior efficiency and generalizability compared to existing methods.
  • The method improves high-resolution disparity estimation in stereo matching and offers practical benefits for efficient real-time processing in computer vision systems.

Superpixel Segmentation with Fully Convolutional Networks

The paper "Superpixel Segmentation with Fully Convolutional Networks" introduces a method for integrating superpixel segmentation into deep neural networks using a Fully Convolutional Network (FCN). By addressing both computational efficiency and accuracy, this approach demonstrates potential improvements for dense prediction tasks in computer vision, such as stereo matching.

Overview

Superpixels are essential in computer vision for reducing the complexity of image data by grouping similar pixels. Traditional superpixel algorithms face challenges when integrated into CNNs primarily due to convolution operations being inefficient on irregular grids. The proposed method circumvents this by utilizing a novel strategy that predicts superpixels directly on regular image grids using an FCN architecture.

Methodology

The method leverages a simple encoder-decoder Neural Network to predict pixel-superpixel association scores, which sidesteps the inefficiencies of performing convolution on irregular superpixel grids. This approach is contrasted with previous techniques like SSN, which use pixel features derived from CNNs for clustering. Here, superpixel segmentation is reformulated as a grid-based association task, leading to competitive results with a simplified and computationally efficient network design.

The loss functions are designed to optimize pixel grouping based on chosen properties (such as color or semantic labels), with a spatial coherence constraint reminiscent of SLIC's spatial regularization, making the approach adaptable for different downstream vision tasks.

Experimental Results

The proposed FCN achieves state-of-the-art performance on benchmark datasets, including BSDS500 and NYUv2, demonstrating superior generalizability and runtime efficiency compared to existing methods like SEAL and SSN. It efficiently balances boundary adherence and superpixel compactness, making it suitable for applications beyond the dataset it was trained on.

Implications for Stereo Matching

The paper extends the utility of superpixels to stereo matching tasks. A modified PSMNet is employed to incorporate the superpixel-based downsampling/upsampling mechanism. This integration facilitates high-resolution disparity estimation, outperforming traditional bilinear upsampling and improving accuracy on SceneFlow, HR-VS, and Middlebury-v3 datasets. The joint training approach further enhances predictive performance, indicating beneficial mutual leveraging of superpixel segmentation and disparity prediction network components.

Conclusion

This work formulates a computationally efficient method for superpixel segmentation through deep learning and suggests an application utility in improving dense prediction tasks, with specific focus on stereo matching. The strategy provides a balance between maintaining image boundary precision and facilitating significant computation reduction across high-resolution tasks. Future developments could involve extending this methodology to other dense prediction challenges like semantic segmentation, optical flow estimation, and enhancing methods for real-time applications in varying environments.

The implications of this work are notably practical, indicating potential pathways for efficiently handling high-resolution input images in real-time systems and providing quality output in systems constrained by computational resources.