Local Label Descriptor for Example Based Semantic Image Labeling

  • Yiqing Yang
  • Zhouyuan Li
  • Li Zhang
  • Christopher Murphy
  • Jim Ver Hoeve
  • Hongrui Jiang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7578)

Abstract

In this paper we introduce the concept of local label descriptor, which is a concatenation of label histograms for each cell in a patch. Local label descriptors alleviate the label patch misalignment issue in combining structured label predictions for semantic image labeling. Given an input image, we solve for a label map whose local label descriptors can be approximated as a sparse convex combination of exemplar label descriptors in the training data, where the sparsity is regularized by the similarity measure between the local feature descriptor of the input image and that of the exemplars in the training data set. Low-level image over-segmentation can be incorporated into our formulation to improve efficiency. Our formulation and algorithm compare favorably with the baseline method on the CamVid and Barcelona datasets.

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References

  1. 1.
    Kontschieder, P., Bulo, S., Bischof, H., Pelillo, M.: Structured class-labels in random forests for semantic image labelling. In: ICCV (2011)Google Scholar
  2. 2.
    Brostow, G.J., Shotton, J., Fauqueur, J., Cipolla, R.: Segmentation and Recognition Using Structure from Motion Point Clouds. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 44–57. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Tighe, J., Lazebnik, S.: SuperParsing: Scalable Nonparametric Image Parsing with Superpixels. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 352–365. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    He, X., Zemel, R., Carreira-Perpinan, M.: Multiscale conditional random fields for image labeling. In: CVPR (2004)Google Scholar
  5. 5.
    Shotton, J., Winn, J.M., Rother, C., Criminisi, A.: textonBoost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 1–15. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Kohli, P., Ladicky, L., Torr, P.: Robust higher order potentials for enforcing label consistency. In: CVPR (2008)Google Scholar
  7. 7.
    Galleguillos, C., Rabinovich, A., Belongie, S.: Object categorization using co-occurrence, location and appearance. In: CVPR (2008)Google Scholar
  8. 8.
    Ladicky, L., Russell, C., Kohli, P., Torr, P.H.S.: Graph Cut Based Inference with Co-occurrence Statistics. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 239–253. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Gonfaus, J., Boix, X., van de Weijer, J., Bagdanov, A., Serrat, J., Gonzandlez, J.: Harmony potentials for joint classification and segmentation. In: CVPR (2010)Google Scholar
  10. 10.
    Torralba, A., Murphy, K., Freeman, W., Rubin, M.: Context-based vision system for place and object recognition. In: ICCV (2003)Google Scholar
  11. 11.
    Rabinovich, A., Vedaldi, A., Galleguillos, C., Wiewiora, E., Belongie, S.: Objects in context. In: ICCV (2007)Google Scholar
  12. 12.
    Toyoda, T., Hasegawa, O.: Random field model for integration of local information and global information. TPAMI 30 (2008)Google Scholar
  13. 13.
    Ladický, Ľ., Sturgess, P., Alahari, K., Russell, C., Torr, P.H.S.: What, Where and How Many? Combining Object Detectors and CRFs. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 424–437. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  14. 14.
    Russell, B.C., Torralba, A., Liu, C., Fergus, R., Freeman, W.T.: Object recognition by scene alignment. In: NIPS (2007)Google Scholar
  15. 15.
    Liu, C., Yuen, J., Torralba, A.: Nonparametric scene parsing via label transfer. TPAMI 33 (2011)Google Scholar
  16. 16.
    Socher, R., Lin, C.C.Y., Ng, A.Y., Manning, C.D.: Parsing natural scenes and natural language with recursive neural networks. In: ICML (2011)Google Scholar
  17. 17.
    Huang, Q., Han, M., Wu, B., Ioffe, S.: A hierarchical conditional random field model for labeling and segmenting images of street scenes. In: CVPR (2011)Google Scholar
  18. 18.
    Arora, S., Hazan, E., Kale, S.: The multiplicative weights update method: a meta algorithm and applications. Technical report, Princeton University (2005)Google Scholar
  19. 19.
    Kumar, N., Zhang, L., Nayar, S.K.: What Is a Good Nearest Neighbors Algorithm for Finding Similar Patches in Images? In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 364–378. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  20. 20.
    Andoni, A., Indyk, P.: Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Commun. ACMGoogle Scholar
  21. 21.
    Barnes, C., Shechtman, E., Finkelstein, A., Goldman, D.B.: PatchMatch: A randomized correspondence algorithm for structural image editing. ACM Transactions on Graphics, Proc. SIGGRAPH (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yiqing Yang
    • 1
  • Zhouyuan Li
    • 1
  • Li Zhang
    • 1
  • Christopher Murphy
    • 2
  • Jim Ver Hoeve
    • 1
  • Hongrui Jiang
    • 1
  1. 1.University of Wisconsin-MadisonUSA
  2. 2.University of CaliforniaDavisUSA

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