Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Convolutional Oriented Boundaries

Authors : Kevis-Kokitsi Maninis, Jordi Pont-Tuset, Pablo Arbeláez, Luc Van Gool

Published in: Computer Vision – ECCV 2016

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter RepMatch: Robust Feature Matching and Pose for Reconstructing Modern Cities
next chapter Superpixel Convolutional Networks Using Bilateral Inceptions
Literature
1.
Kokkinos, I.: Pushing the boundaries of boundary detection using deep learning. In: ICLR (2016)
2.
Xie, S., Tu, Z.: Holistically-nested edge detection. In: ICCV (2015)
3.
Bertasius, G., Shi, J., Torresani, L.: Deepedge: a multi-scale bifurcated deep network for top-down contour detection. In: CVPR (2015)
4.
Bertasius, G., Shi, J., Torresani, L.: High-for-low and low-for-high: efficient boundary detection from deep object features and its applications to high-level vision. In: ICCV (2015)
5.
Shen, W., Wang, X., Wang, Y., Bai, X., Zhang, Z.: Deepcontour: a deep convolutional feature learned by positive-sharing loss for contour detection. In: CVPR (2015)
6.
Ganin, Y., Lempitsky, V.: \(N^4\)-Fields: Neural Network Nearest Neighbor Fields for Image Transforms. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9004, pp. 536–551. Springer, Heidelberg (2015)
7.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)
8.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: CVPR (2015)
9.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)
10.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
11.
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: ICCV (2001)
12.
13.
Hariharan, B., Arbeláez, P., Bourdev, L., Maji, S., Malik, J.: Semantic contours from inverse detectors. In: ICCV (2011)
14.
Mottaghi, R., Chen, X., Liu, X., Cho, N.G., Lee, S.W., Fidler, S., Urtasun, R., Yuille, A.: The role of context for object detection and semantic segmentation in the wild. In: CVPR (2014)
15.
Arbeláez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. TPAMI 33(5), 898–916 (2011)CrossRef
16.
Pont-Tuset, J., Arbeláez, P., Barron, J., Marques, F., Malik, J.: Multiscale combinatorial grouping for image segmentation and object proposal generation. In: TPAMI (2016)
17.
Maninis, K., Pont-Tuset, J., Arbeláez, P., Gool, L.V.: Deep retinal image understanding. In: MICCAI (2016)
18.
Bertasius, G., Shi, J., Torresani, L.: Semantic segmentation with boundary neural fields. In: CVPR (2016)
19.
Khoreva, A., Benenson, R., Omran, M., Hein, M., Schiele, B.: Weakly supervised object boundaries. In: CVPR (2016)
20.
Li, Y., Paluri, M., Rehg, J.M., Dollár, P.: Unsupervised learning of edges. In: CVPR (2016)
21.
Yang, J., Price, B., Cohen, S., Lee, H., Yang, M.H.: Object contour detection with a fully convolutional encoder-decoder network. In: CVPR (2016)
22.
Najman, L., Schmitt, M.: Geodesic saliency of watershed contours and hierarchical segmentation. TPAMI 18(12), 1163–1173 (1996)CrossRef
23.
24.
Dollár, P., Zitnick, C.L.: Structured forests for fast edge detection. In: ICCV (2013)
25.
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding (2014). arXiv preprint arXiv:​1408.​5093
26.
Martin, D., Fowlkes, C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. TPAMI 26(5), 530–549 (2004)CrossRef
27.
Pont-Tuset, J., Marques, F.: Supervised evaluation of image segmentation and object proposal techniques. TPAMI 38(7), 1465–1478 (2016)CrossRef
28.
Ren, Z., Shakhnarovich, G.: Image segmentation by cascaded region agglomeration. In: CVPR (2013)
29.
Ren, Z., Shakhnarovich, G.: Image segmentation by cascaded region agglomeration. In: CVPR (2013)
30.
Shi, J., Malik, J.: Normalized cuts and image segmentation. TPAMI 22(8), 888–905 (2000)CrossRef
31.
Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. IJCV 59, 167–181 (2004)
32.
Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. TPAMI 24(5), 603–619 (2002)CrossRef
33.
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR (2014)
34.
35.
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with regionproposal networks.In: NIPS (2015)
36.
Humayun, A., Li, F., Rehg, J.M.: The middle child problem: revisiting parametric min-cut and seeds for object proposals. In: ICCV (2015)
37.
Krähenbühl, P., Koltun, V.: Learning to propose objects. In: CVPR (2015)
38.
Krähenbühl, P., Koltun, V.: Geodesic Object Proposals. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 725–739. Springer, Heidelberg (2014)
39.
Uijlings, J.R.R., Sande, K.E.A., Gevers, T., Smeulders, A.W.M.: Selective search for object recognition. IJCV 104(2), 154–171 (2013)CrossRef
40.
Rantalankila, P., Kannala, J., Rahtu, E.: Generating object segmentation proposals using global and local search. In: CVPR (2014)
41.
Humayun, A., Li, F., Rehg, J.M.: RIGOR: Recycling Inference in Graph Cuts for generating Object Regions. In: CVPR (2014)
42.
Hosang, J., Benenson, R., Dollár, P., Schiele, B.: What makes for effective detection proposals? TPAMI 38(4), 814–830 (2016)CrossRef
43.
Pont-Tuset, J., Van Gool, L.: Boosting object proposals: From Pascal to COCO. In: ICCV (2015)
44.
Lin, T., Maire, M., Belongie, S., Bourdev, L.D., Girshick, R.B., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft COCO: common objects in context (2014). arXiv:​1405.​0312
Metadata
Title
Convolutional Oriented Boundaries
Authors
Kevis-Kokitsi Maninis
Jordi Pont-Tuset
Pablo Arbeláez
Luc Van Gool
Copyright Year
2016
Book
Computer Vision – ECCV 2016

Print ISBN: 978-3-319-46447-3

Electronic ISBN: 978-3-319-46448-0

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46448-0_35

Premium Partner

    Image Credits