Advertisement

Indoor/Outdoor Classification with Multiple Experts

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

Abstract

Indoor/outdoor classification is a fundamental step toward scene understanding. When data become more diverse, traditional approaches are not able to efficiently provide robust performance. In this chapter, we will firstly review the weakness of existing approaches and then propose a systematic machine-learning approach, Expert Decision Fusion (EDF) to obtain robust classification performance.

Keywords

Big visual data Indoor/outdoor classification Expert decision fusion Structured machine learning system 

References

  1. 1.
    Battiato, S., Curti, S., Cascia, M.L., Tortora, M., Scordato, E.: Depth map generation by image classification. Proc. SPIE 5302, 95–104 (2004)CrossRefGoogle Scholar
  2. 2.
    Bianco, S., Ciocca, G., Cusano, C., Schettini, R.: Improving color constancy using indoor-outdoor image classification. IEEE Trans. Image Process. 17(12), 2381–2392 (2008)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Bosch, A., Zisserman, A., Muñoz, X.: Scene classification via plsa. In: Computer Vision-ECCV 2006. Springer, New York (2006), pp. 517–530Google Scholar
  4. 4.
    Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recognit. 37(9), 1757–1771 (2004)CrossRefGoogle Scholar
  5. 5.
    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
  6. 6.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  7. 7.
    Daubechies, I.: Ten Lectures on Wavelets, vol. 61. SIAM (1992)Google Scholar
  8. 8.
    Deng, L., Yu, D., Platt, J.: Scalable stacking and learning for building deep architectures. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012, pp. 2133–2136. IEEE (2012)Google Scholar
  9. 9.
    Džeroski, S., Ženko, B.: Is combining classifiers with stacking better than selecting the best one? Mach. Learn. 54(3), 255–273 (2004)CrossRefzbMATHGoogle Scholar
  10. 10.
    Fei-Fei, L., Perona, P.: A bayesian hierarchical model for learning natural scene categories. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, vol. 2, pp. 524–531. IEEE (2005)Google Scholar
  11. 11.
    Fogel, I., Sagi, D.: Gabor filters as texture discriminator. Biol. Cybern. 61(2), 103–113 (1989)CrossRefGoogle Scholar
  12. 12.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice Hall, New Jersey (2002), pp. 462–463Google Scholar
  13. 13.
    Gray, R.M., Olshen, R.A.: Vector quantization and density estimation. In: Proceedings on Compression and Complexity of Sequences 1997. IEEE (1997), pp. 172–193Google Scholar
  14. 14.
    He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)CrossRefGoogle Scholar
  15. 15.
    Huang, J., Liu, Z., Wang, Y.: Joint scene classification and segmentation based on hidden markov model. IEEE Trans. Multim. 7(3), 538–550 (2005)CrossRefGoogle Scholar
  16. 16.
    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
  17. 17.
    Johnson, J.B.: Thermal agitation of electricity in conductors. Phys. Rev. 32(1), 97 (1928)CrossRefGoogle Scholar
  18. 18.
    Karpenko, A., Aarabi, P.: Tiny videos: Non-parametric content-based video retrieval and recognition. In: Tenth IEEE International Symposium on Multimedia, 2008. ISM 2008. IEEE (2008), pp. 619–624Google Scholar
  19. 19.
    Kim, W., Park, J., Kim, C.: A novel method for efficient indoor-outdoor image classification. J. Signal Process. Syst. 61(3), 251–258 (2010)CrossRefGoogle Scholar
  20. 20.
    Kohonen, T., Kangas, J., Laaksonen, J., Torkkola, K.: Lvq\(\_\)pak: a software package for the correct application of learning vector quantization algorithms. In: International Joint Conference on Neural Networks, 1992. IJCNN, vol. 1. IEEE (1992), pp. 725–730Google Scholar
  21. 21.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, vol. 2. IEEE (2006), pp. 2169–2178Google Scholar
  22. 22.
    Li, L.J., Su, H., Fei-Fei, L., Xing, E.P.: Object bank: A high-level image representation for scene classification and semantic feature sparsification. In: Advances in Neural Information Processing Systems, pp. 1378–1386 (2010)Google Scholar
  23. 23.
    Liu, C., Yuen, J., Torralba, A.: Nonparametric scene parsing: label transfer via dense scene alignment. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE (2009), pp. 1972–1979Google Scholar
  24. 24.
    Maini, R., Aggarwal, H.: Study and comparison of various image edge detection techniques. Int. J. Image Process. (IJIP) 3(1), 1–11 (2009)Google Scholar
  25. 25.
    Mao, J., Jain, A.K.: Texture classification and segmentation using multiresolution simultaneous autoregressive models. Pattern Recognit. 25(2), 173–188 (1992)CrossRefGoogle Scholar
  26. 26.
    Murphy, K., Torralba, A., Freeman, W., et al.: Using the forest to see the trees: a graphical model relating features, objects and scenes. Adv. Neural Inf. Process. Syst. 16, 1499–1506 (2003)Google Scholar
  27. 27.
    Ohta, Y.I., Kanade, T., Sakai, T.: Color information for region segmentation. Comput. Graph. Image Process. 13(3), 222–241 (1980)CrossRefGoogle Scholar
  28. 28.
    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
  29. 29.
    Pavlopoulou, C., Yu, S.X.: Indoor-outdoor classification with human accuracies: Image or edge gist? In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2010. IEEE (2010), pp. 41–47Google Scholar
  30. 30.
    Picard, R.W., Kabir, T., Liu, F.: Real-time recognition with the entire brodatz texture database. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1993. Proceedings CVPR’93, 1993. IEEE (1993), pp. 638–639Google Scholar
  31. 31.
    Quattoni, A., Torralba, A.: Recognizing indoor scenes. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2009)Google Scholar
  32. 32.
    Rissanen, J.: Stochastic complexity. J. R. Stat. Soc. Ser. B (Methodological), 223–239 (1987)Google Scholar
  33. 33.
    Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: Labelme: a database and web-based tool for image annotation. Int. J. Comput. Vis. 77(1–3), 157–173 (2008)CrossRefGoogle Scholar
  34. 34.
    Serrano, N., Savakis, A., Luo, A.: A computationally efficient approach to indoor/outdoor scene classification. In: 16th International Conference on Pattern Recognition, Proceedings, vol. 4, pp. 146–149 (2002)Google Scholar
  35. 35.
    Stella, X.Y., Zhang, H., Malik, J.: Inferring spatial layout from a single image via depth-ordered grouping. In: CVPR Workshop (2008)Google Scholar
  36. 36.
    Swain, M.J., Ballard, D.H.: Color indexing. Int. J. Comput. Vis. 7(1), 11–32 (1991)CrossRefGoogle Scholar
  37. 37.
    Szummer, M., Picard, R.W.: Indoor-outdoor image classification. In: IEEE International Workshop on Content-Based Access of Image and Video Database on, pp. 42–51. IEEE (1998)Google Scholar
  38. 38.
    Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Sixth International Conference on Computer Vision, 1998. IEEE (1998), pp. 839–846Google Scholar
  39. 39.
    Torralba, A., Fergus, R., Freeman, W.T.: 80 million tiny images: a large data set for nonparametric object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 30(11), 1958–1970 (2008)CrossRefGoogle Scholar
  40. 40.
    Vailaya, A., Figueiredo, M., Jain, A., Zhang, H.J.: Content-based hierarchical classification of vacation images. IEEE International Conference on Multimedia Computing and Systems, vol. 1, pp. 518–523 (1999)Google Scholar
  41. 41.
    Vailaya, A., Jain, A., Zhang, H.J.: On image classification: city images vs. landscapes. Pattern Recognit. 31(12), 1921–1935 (1998)CrossRefGoogle Scholar
  42. 42.
    Vailaya, A., Figueiredo, M.A., Jain, A.K., Zhang, H.J.: Image classification for content-based indexing. IEEE Trans. Image Process. 10(1), 117–130 (2001)CrossRefzbMATHGoogle Scholar
  43. 43.
    Vasconcelos, N., Lippman, A.: Library-based coding: a representation for efficient video compression and retrieval. In: Data Compression Conference, 1997. DCC’97. Proceedings. IEEE (1997), pp. 121–130Google Scholar
  44. 44.
    Vogel, J., Schiele, B.: Semantic modeling of natural scenes for content-based image retrieval. Int. J. Comput. Vis. 72(2), 133–157 (2007)CrossRefGoogle Scholar
  45. 45.
    Wolpert, D.H.: Stacked generalization. Neural Netw. 5(2), 241–259 (1992)MathSciNetCrossRefGoogle Scholar
  46. 46.
    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
  47. 47.
    Xiao, J., Hays, J., Ehinger, K.A., Oliva, A., Torralba, A.: Sun database: large-scale scene recognition from abbey to zoo. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010. IEEE (2010), pp. 3485–3492Google Scholar
  48. 48.
    Zhang, L., Li, M., Zhang, H.J.: Boosting image orientation detection with indoor vs. outdoor classification. In: Sixth IEEE Workshop on Applications of Computer Vision, 2002. (WACV 2002). Proceedings. IEEE (2002), pp. 95–99Google 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