Advertisement

Introduction

  • Chen Chen
  • Yuzhuo Ren
  • C.-C. Jay Kuo
Chapter
Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)

Abstract

Large-scale visual data understanding is a long-standing popular problem in the computer vision society. When more visual data become available, problems become more challenging to traditional approaches. In this chapter, we will briefly review three important research problems, indoor/outdoor classification, outdoor scene categorization and geometric labeling. In addition, we will provide an overview of the book and its perspective benefits to the readers.

Keywords

Big visual data Indoor/Outdoor classification Outdoor scene classification Geometric labeling Decision fusion Structured machine learning system Contour-guided color palette Semantic segmentation Context-aware labeling Global attributes 

References

  1. 1.
    Boyd, C.R., Tolson, M.A., Copes, W.S.: Evaluating trauma care: the triss method. J. Trauma-Inj., Infect., Crit. Care 27(4), 370–378 (1987)CrossRefGoogle Scholar
  2. 2.
    Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)Google Scholar
  3. 3.
    Chatzichristofis, S.A., Boutalis, Y.S.: Cedd: color and edge directivity descriptor: a compact descriptor for image indexing and retrieval. In: Computer Vision Systems, pp. 312–322. Springer (2008)Google Scholar
  4. 4.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2005, vol. 1, pp. 886–893 (2005)Google Scholar
  5. 5.
    Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34(4), 743–761 (2012)CrossRefGoogle Scholar
  6. 6.
    Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)CrossRefGoogle Scholar
  7. 7.
    Freund, Y., Schapire, R.E., et al.: Experiments with a new boosting algorithm. In: ICML, vol. 96, pp. 148–156 (1996)Google Scholar
  8. 8.
    Gupta, A., Efros, A.A., Hebert, M.: Blocks world revisited: image understanding using qualitative geometry and mechanics. In: Computer Vision ECCV 2010, pp. 482–496. Springer (2010)Google Scholar
  9. 9.
    Gupta, A., Hebert, M., Kanade, T., Blei, D.M.: Estimating spatial layout of rooms using volumetric reasoning about objects and surfaces. In: Advances in Neural Information Processing Systems, pp. 1288–1296 (2010)Google Scholar
  10. 10.
    Hoiem, D., Efros, A., Hebert, M., et al.: Geometric context from a single image. In: Tenth IEEE International Conference on Computer Vision. ICCV 2005, vol. 1, pp. 654–661. IEEE (2005)Google Scholar
  11. 11.
    Jégou, H., Douze, M., Schmid, C.: Improving bag-of-features for large scale image search. Int. J. Comput. Vis. 87(3), 316–336 (2010)CrossRefGoogle Scholar
  12. 12.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference, vol. 2, pp. 2169–2178 (2006)Google Scholar
  13. 13.
    Li, L.J., Fei-Fei, L.: What, where and who? classifying events by scene and object recognition. In: IEEE 11th International Conference on Computer Vision. ICCV 2007, pp. 1–8. IEEE (2007)Google Scholar
  14. 14.
    Lienhart, R., Maydt, J.: An extended set of haar-like features for rapid object detection. In: 2002 International Conference on Image Processing. Proceedings., vol. 1, pp. I–900. IEEE (2002)Google Scholar
  15. 15.
    Lim, J.H., Jin, J.S.: A structured learning framework for content-based image indexing and visual query. Multimed. Syst. 10(4), 317–331 (2005)CrossRefGoogle Scholar
  16. 16.
    Liu, X., Zhao, Y., Zhu, S.C.: Single-view 3D scene parsing by attributed grammar. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 684–691. IEEE (2014)Google Scholar
  17. 17.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  18. 18.
    Niebles, J.C., Fei-Fei, L.: A hierarchical model of shape and appearance for human action classification. In: IEEE Conference on Computer Vision and Pattern Recognition. CVPR’07, pp. 1–8. IEEE (2007)Google Scholar
  19. 19.
    Niebles, J.C., Wang, H., Fei-Fei, L.: Unsupervised learning of human action categories using spatial-temporal words. Int. J. Comput. Vis. 79(3), 299–318 (2008)CrossRefGoogle Scholar
  20. 20.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42(3), 145–175 (2001)CrossRefzbMATHGoogle Scholar
  21. 21.
    Quattoni, A., Torralba, A.: Recognizing indoor scenes. In: Computer vision and pattern recognition (CVPR), 2009 IEEE conference (2009)Google Scholar
  22. 22.
    van Gemert, J.C., Geusebroek, J.M., Veenman, C.J., Smeulders, A.W.: Kernel codebooks for scene categorization. In: Computer Vision-ECCV 2008, pp. 696–709. Springer (2008)Google Scholar
  23. 23.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 1, pp. I–511. IEEE (2001)Google Scholar
  24. 24.
    Wu, J., Rehg, J.M.: Centrist: a visual descriptor for scene categorization. IEEE Trans. Pattern Anal. Mach. Intell. 33(8), 1489–1501 (2011)CrossRefGoogle Scholar
  25. 25.
    Xiao, J., Hays, J., Ehinger, K.A., Oliva, A., Torralba, A.: Sun database: large-scale scene recognition from abbey to zoo. In: Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference, pp. 3485–3492. IEEE (2010)Google Scholar
  26. 26.
    Yan, J., Zhang, X., Lei, Z., Liao, S., Li, S.Z.: Robust multi-resolution pedestrian detection in traffic scenes. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3033–3040. IEEE (2013)Google Scholar
  27. 27.
    Zhou, H., Yuan, Y., Shi, C.: Object tracking using sift features and mean shift. Comput. Vis. Image Underst. 113(3), 345–352 (2009)CrossRefGoogle Scholar

Copyright information

© The Author(s) 2016

Authors and Affiliations

  1. 1.Department of Electrical EngineeringUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.University of Southern CaliforniaLos AngelesUSA

Personalised recommendations