Convolutional Oriented Boundaries

We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification Convolutional Neural Networks (CNNs). COB is computationally efficient, because it requires a single CNN forward pass for contour detection and it uses a novel sparse boundary representation for hierarchical segmentation; it gives a significant leap in performance over the state-of-the-art, and it generalizes very well to unseen categories and datasets. Particularly, we show that learning to estimate not only contour strength but also orientation provides more accurate results. We perform extensive experiments on BSDS, PASCAL Context, PASCAL Segmentation, and MS-COCO, showing that COB provides state-of-the-art contours, region hierarchies, and object proposals in all datasets.

[1]  Laurent Najman,et al.  Geodesic Saliency of Watershed Contours and Hierarchical Segmentation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[4]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Jitendra Malik,et al.  Learning to detect natural image boundaries using local brightness, color, and texture cues , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Jianguo Zhang,et al.  The PASCAL Visual Object Classes Challenge , 2006 .

[7]  Luc Van Gool,et al.  The 2005 PASCAL Visual Object Classes Challenge , 2005, MLCW.

[8]  Charless C. Fowlkes,et al.  Contour Detection and Hierarchical Image Segmentation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Subhransu Maji,et al.  Semantic contours from inverse detectors , 2011, 2011 International Conference on Computer Vision.

[10]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[11]  Koen E. A. van de Sande,et al.  Selective Search for Object Recognition , 2013, International Journal of Computer Vision.

[12]  C. Lawrence Zitnick,et al.  Structured Forests for Fast Edge Detection , 2013, 2013 IEEE International Conference on Computer Vision.

[13]  Gregory Shakhnarovich,et al.  Image Segmentation by Cascaded Region Agglomeration , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Sanja Fidler,et al.  The Role of Context for Object Detection and Semantic Segmentation in the Wild , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  James M. Rehg,et al.  RIGOR: Reusing Inference in Graph Cuts for Generating Object Regions , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[18]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[19]  Esa Rahtu,et al.  Generating Object Segmentation Proposals Using Global and Local Search , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  智一 吉田,et al.  Efficient Graph-Based Image Segmentationを用いた圃場図自動作成手法の検討 , 2014 .

[21]  Vladlen Koltun,et al.  Geodesic Object Proposals , 2014, ECCV.

[22]  Victor S. Lempitsky,et al.  N4-Fields: Neural Network Nearest Neighbor Fields for Image Transforms , 2014, ArXiv.

[23]  Jianbo Shi,et al.  DeepEdge: A multi-scale bifurcated deep network for top-down contour detection , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Yan Wang,et al.  DeepContour: A deep convolutional feature learned by positive-sharing loss for contour detection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  James M. Rehg,et al.  The Middle Child Problem: Revisiting Parametric Min-Cut and Seeds for Object Proposals , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[26]  Jianbo Shi,et al.  High-for-Low and Low-for-High: Efficient Boundary Detection from Deep Object Features and Its Applications to High-Level Vision , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[27]  Saining Xie,et al.  Holistically-Nested Edge Detection , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[28]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Vladlen Koltun,et al.  Learning to propose objects , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[30]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[31]  Iasonas Kokkinos,et al.  Pushing the Boundaries of Boundary Detection using Deep Learning , 2015, ICLR 2016.

[32]  Qiyang Zhao,et al.  Segmenting natural images with the least effort as humans , 2015, BMVC.

[33]  Luc Van Gool,et al.  Boosting Object Proposals: From Pascal to COCO , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[34]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[35]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[36]  Zhuowen Tu,et al.  Deeply-Supervised Nets , 2014, AISTATS.

[37]  Jordi Pont-Tuset,et al.  Supervised Evaluation of Image Segmentation and Object Proposal Techniques , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  Jianbo Shi,et al.  Semantic Segmentation with Boundary Neural Fields , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[39]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[40]  Bernt Schiele,et al.  Weakly Supervised Object Boundaries , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[41]  Bernt Schiele,et al.  What Makes for Effective Detection Proposals? , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[42]  Honglak Lee,et al.  Object Contour Detection with a Fully Convolutional Encoder-Decoder Network , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[43]  Luc Van Gool,et al.  Deep Retinal Image Understanding , 2016, MICCAI.

[44]  James M. Rehg,et al.  Unsupervised Learning of Edges , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[45]  Jonathan T. Barron,et al.  Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.