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:
A Comparative Study for Known Item Visual Search Using Position Color Feature Signatures Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Unsupervised Multiple Object Cosegmentation via Ensemble MIML Learning

Authors : Weichen Yang, Zhengxing Sun, Bo Li, Jiagao Hu, Kewei Yang

Published in: MultiMedia Modeling

Publisher: Springer International Publishing

Log in

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

search-config
loading …

Abstract

Multiple foreground cosegmentation (MFC) has being a new research topic recently in computer vision. This paper proposes a framework of unsupervised multiple object cosegmentation, which is composed of three components: unsupervised label generation, saliency pseudo-annotation and cosegmentation based on MIML learning. Based on object detection, unsupervised label generation is done in terms of the two-stage object clustering method, to obtain accurate consistent label between common objects without any user intervention. Then, the object label is propagated to the object saliency coming from saliency detection method, to finish saliency pseudo-annotation. This makes an unsupervised MFC problem as a supervised multi-instance multi-label (MIML) learning problem. Finally, an ensemble MIML framework is introduced to achieve image cosegmentation based on random feature selection. The experimental results on data sets ICoseg and FlickrMFC demonstrated the effectiveness of the proposed approach.

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 The Perceptual Lossless Quantization of Spatial Parameter for 3D Audio Signals
next chapter Using Object Detection, NLP, and Knowledge Bases to Understand the Message of Images
Literature
1.
Kim, G., Xing, E.P.: On multiple foreground cosegmentation. In: IEEE CVPR, pp. 837–844 (2012)
2.
Li, H., Meng, F.: Unsupervised multiclass region cosegmentation via ensemble clustering and energy minimization. IEEE Trans. Circ. Syst. Video Technol. 24(5), 789–801 (2014)CrossRef
3.
Li, K., Zhang, J., Tao, W.: Unsupervised co-segmentation for indefinite number of common foreground objects. IEEE Trans. Image Process. 25(4), 1898–1909 (2016)MathSciNetCrossRef
4.
Meng, F., Li, H.: Constrained directed graph clustering and segmentation propagation for multiple foregrounds cosegmentation. IEEE Trans. Circ. Syst. Video Technol. 25(11), 1735–1748 (2015)CrossRef
5.
Li, L., Fei, X.: Unsupervised multi-class co-segmentation via joint object detection and segmentation with energy minimization. In: MIPPR, pp. 9812–9814 (2015)
6.
Chang, H.S., Wang, Y.C.F.: Optimizing the decomposition for multiple foreground cosegmentation. Comput. Vis. Image Underst. 141, 18–27 (2015)CrossRef
7.
Rother, C., Minka, T.: Cosegmentation of image pairs by histogram matching-incorporating a global constraint into MRFs. In: IEEE CVPR, pp. 993–1000 (2006)
8.
Mukherjee, L., Singh, V., Dyer, C.R.: Half-integrality based algorithms for cosegmentation of images. In: IEEE CVPR, pp. 2028–2035 (2009)
9.
Kim, G., Xing, E.P., Fei-Fei, L., Kanade, T.: Distributed cosegmentation via submodular optimization on anisotropic diffusion. In: IEEE ICCV, pp. 169–176 (2011)
10.
Wang, F., Huang, Q., Ovsjanikov, M., Guibas, L.J.: Unsupervised multi-class joint image segmentation. In: IEEE CVPR, pp. 3142–3149 (2014)
11.
Ma, T., Jan Latecki, L.: Graph transduction learning with connectivity constraints with application to multiple foreground cosegmentation. In: IEEE CVPR, pp. 1955–1962(2013)
12.
Zhu, H., Lu, J., Cai, J., Zheng, J., Thalmann, N.M.: Multiple foreground recognition and cosegmentation: an object-oriented CRF model with robust higher-order potentials. In: IEEE WACV, pp. 485–492 (2014)
13.
Zitnick, C.L., Dollár, P.: Edge boxes: locating object proposals from edges. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 391–405. Springer, Heidelberg (2014). doi:10.​1007/​978-3-319-10602-1_​26
14.
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE CVPR, pp. 3431–3440 (2015)
15.
16.
Zhu, W., Liang, S., Wei, Y., Sun, J.: Saliency optimization from robust background detection. In: IEEE CVPR, pp. 2814–2821 (2014)
17.
Cheng, M.M., Mitra, N.J., Huang, X., Torr, P.H., Hu, S.M.: Global contrast based salient region detection. IEEE TPAMI 37(3), 569–582 (2015)CrossRef
18.
Achanta, R., Smith, S.A.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE TPAMI 34(11), 2274–2282 (2012)CrossRef
19.
Zhou, Z.H., Zhang, M.L.: Multi-instance multi-label learning with application to scene classification. In: NIPS, pp. 1609–1616 (2006)
20.
Briggs, F., Fern, X.Z., Raich, R.: Rank-loss support instance machines for MIML instance annotation. In: ACM SIGKDD, pp. 534–542 (2012)
21.
Batra, D., Kowdle, A.: iCoseg: interactive co-segmentation with intelligent scribble guidance. In: IEEE CVPR, pp. 3169–3176 (2010)
22.
Joulin, A., Bach, F., Ponce, J.: Discriminative clustering for image co-segmentation. In: IEEE CVPR, pp. 1943–1950 (2010)
23.
Vicente, S., Rother, C., Kolmogorov, V.: Object cosegmentation. In: IEEE CVPR, pp. 2217–2224 (2011)
24.
Rubinstein, M., Joulin, A., Koft, J., Liu, C.: Object co-segmentation based on shortest path algorithm and saliency model. IEEE Trans. Multimedia 14(5), 1429–1441 (2012)CrossRef
25.
Rubinstein, M., Joulin, A., Kopf, J., Liu, C.: Unsupervised joint object discovery and segmentation in internet images. In: IEEE CVPR, pp. 1939–1946 (2013)
Metadata
Title
Unsupervised Multiple Object Cosegmentation via Ensemble MIML Learning
Authors
Weichen Yang
Zhengxing Sun
Bo Li
Jiagao Hu
Kewei Yang
Copyright Year
2017
Book
MultiMedia Modeling

Print ISBN: 978-3-319-51813-8

Electronic ISBN: 978-3-319-51814-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-51814-5_33