SemiContour: A Semi-supervised Learning Approach for Contour Detection

By Zizhao Zhang, Fuyong Xing, Xiaoshuang Shi, Lin F. Yang. 2016

50 citations recorded by the source corpus.

Supervised contour detection methods usually require many labeled training images to obtain satisfactory performance. However, a large set of annotated data might be unavailable or extremely labor intensive. In this paper, we investigate the usage of semi-supervised learning (SSL) to obtain competitive detection accuracy with very limited training data (three labeled images). Specifically, we propose a semi-supervised structured ensemble learning approach for contour detection built on structured random forests (SRF). To allow SRF to be applicable to unlabeled data, we present an effective sparse representation approach to capture inherent structure in image patches by finding a compact and discriminative low-dimensional subspace representation in an unsupervised manner, enabling the incorporation of abundant unlabeled patches with their estimated structured labels to help SRF perform better node splitting. We re-examine the role of sparsity and propose a novel and fast sparse coding algorithm to boost the overall learning efficiency. To the best of our knowledge, this is the first attempt to apply SSL for contour detection. Extensive experiments on the BSDS500 segmentation dataset and the NYU Depth dataset demonstrate the superiority of the proposed method.

Subfields: Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering, Representation & Self-Supervised Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Nicu Sebe
2. Elisa Ricci
3. Wanli Ouyang
4. Xiaogang Wang
5. Luc Van Gool
6. Dan Xu
7. Yi Yang
8. Dacheng Tao

In Industrial and Manufacturing Engineering

1. Xiaogang Wang
2. Wanli Ouyang
3. Dan Xu
4. Nicu Sebe

In Representation & Self-Supervised Learning

1. Wanli Ouyang
2. Xavier Alameda-Pineda
3. Dan Xu
4. Irwin King
5. Zenglin Xu
6. Nicu Sebe
7. Xiangli Yang
8. Zixing Song

Citations in the corpus

• EDTER: Edge Detection with Transformer (2022, neutral): “[49], gPb+NG [19], SE [15], SE+NG+ [20], OEF [21], SemiContour [69], HED [65], RCF [36], AMH-Net [66], LPCB [12], BDCN [22], and PiDiNet [53].”
• A Survey on Deep Semi-Supervised Learning (2021, methodology): “Because of its simplicity and generality, self-training is successfully applied in various tasks such as Named Entity Recognition [210], contour detection [211], machine translation [212], and object detection [213].”
• DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection (2019, background): “Asacrack is visually a linear/curvilinear structure, crack detection can be formulated as line detection, which is a fundamental problem in computer vision [5]–[7].”
• Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction (2017, methodology): “Method ODS OIS AP gPb-ucm [1] .632 .661 .562 OEF [15] .651 .667 – Silberman et al. [33] .658 .661 – SemiContour [46] .680 .700 .690 SE [10] .685 .699 .679 gPb+NG [13] .687 .716 .629 SE+NG+ [14] .710 .723 .738 HED (RGB) [38] .720 .734 .734 HED (HHA) [38] .682 .695 .702 HED (RGB + HHA) [38] .746 .761…”