Exploiting Depth Information for Indoor-Outdoor Scene Classification

  • Ignazio Pillai
  • Riccardo Satta
  • Giorgio Fumera
  • Fabio Roli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6979)

Abstract

A rapid diffusion of stereoscopic image acquisition devices is expected in the next years. Among the different potential applications that depth information can enable, in this paper we focus on its exploitation as a novel information source in the task of scene classification, and in particular to discriminate between indoor and outdoor images. This issue has not been addressed so far in the literature, probably because the extraction of depth information from two-dimensional images is a computationally demanding task. However, new-generation stereo cameras will allow a very fast computation of depth maps. We experimentally show that depth information alone provides a discriminant capability between indoor and outdoor images close to state-of-the art methods based on colour, edge and texture information, and that it allows to improve their performance, when it is used as an additional information source.

Keywords

scene classification depth map indoor-outdoor 

References

  1. 1.
    Alpaydin, E.: Combined 5 x 2 cv F test for comparing supervised classification learning algorithms. Neural Computation 11(8), 1885–1892 (1999)CrossRefGoogle Scholar
  2. 2.
    Battiato, S., Farinella, G.M., Gallo, G., Ravi, D.: Exploiting Textons Distributions on Spatial Hierarchy for Scene Classification. EURASIP Journal on Image and Video Processing (2010)Google Scholar
  3. 3.
    Bianco, S., Ciocca, G., Cusano, C., Schettini, R.: Improving color constancy using indoor-outdoor image classification. IEEE Trans. on Image Processing 17(12), 2381–2392 (2008)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Boutell, M., Luo, J.: Beyond pixels: Exploiting camera metadata for photo classification. Pattern Recognition 38(6), 935–946 (2005)CrossRefGoogle Scholar
  5. 5.
    Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm/
  6. 6.
    Collier, J., Ramirez-Serrano, A.: Environment classification for indoor/outdoor robotic mapping. In: Canadian Conference on Computer and Robot Vision, CRV 2009, pp. 276–283 (May 2009)Google Scholar
  7. 7.
    Deng, D., Zhang, J.: Combining multiple precision-boosted classifiers for indoor-outdoor scene classification. In: Int. Conf. on Information Technology and Applications, vol. 2, pp. 720–725 (2005)Google Scholar
  8. 8.
    Ehinger, K.A., Torralba, A., Oliva, A.: A taxonomy of visual scenes: Typicality ratings and hierarchical classification. Journal of Vision 10(7), 1237 (2010)CrossRefGoogle Scholar
  9. 9.
    Fei-Fei, L., Iyer, A., Koch, C., Perona, P.: What do we perceive in a glance of a real-world scene? Journal of Vision 7(1) (2007)Google Scholar
  10. 10.
    Gupta, L., Pathangay, V., Patra, A., Dyana, A., Das, S.: Indoor versus outdoor scene classification using probabilistic neural network. EURASIP Journ. on Adv. in Signal Processing, Special Issue on Image Perception 2007(1), 123–123 (2007)MATHGoogle Scholar
  11. 11.
    Heitz, G., Gould, S., Saxena, A., Koller, D.: Cascaded classification models: Combining models for holistic scene understanding. In: NIPS (2008)Google Scholar
  12. 12.
    Lee, B.N., Chen, W.Y., Chang, E.Y.: A scalable service for photo annotation, sharing, and search. In: Proc. of the 14th Annual ACM Int. Conf. on Multimedia, pp. 699–702 (2006)Google Scholar
  13. 13.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. Int. Jour. of Computer Vision 42, 145–175 (2001)CrossRefMATHGoogle Scholar
  14. 14.
    Payne, A., Singh, S.: Indoor vs. outdoor scene classification in digital photographs. Pattern Recognition 38(10), 1533–1545 (2005)Google Scholar
  15. 15.
    Saxena, A., Sun, M., Ng, A.: Make3d: Depth perception from a single still image. In: Proc. of The AAAI Conf. on Artificial Intelligence, pp. 1571–1576 (2008)Google Scholar
  16. 16.
    Schettini, R., Brambilla, C., Cusano, C., Ciocca, G.: Automatic classification of digital photographs based on decision forests. International Journal of Pattern Recognition and Artificial Intelligence 18(5), 819–845 (2004)CrossRefGoogle Scholar
  17. 17.
    Serrano, N., Savakis, A., Luo, J.: A computationally efficient approach to indoor/outdoor scene classification. In: ICPR, vol. 4, pp. 146–149 (2002)Google Scholar
  18. 18.
    Serrano, N., Savakis, A.E., Luo, J.: Improved scene classification using efficient low-level features and semantic cues. Pattern Recognition 37(9), 1773–1784 (2004)CrossRefMATHGoogle Scholar
  19. 19.
    Sinha, P., Jain, R.: Classification and annotation of digital photos using optical context data. In: Proc. of The 2008 Int. Conf. on Content-Based Image and Video Retrieval, New York, NY, USA (2008)Google Scholar
  20. 20.
    Szummer, M., Picard, R.W.: Indoor-outdoor image classification. In: Proc. of IEEE Int. Workshop on Content-Based Access of Image and Video Database, pp. 42–51 (1998)Google Scholar
  21. 21.
    Tao, L., Kim, Y.H., Kim, Y.T.: An efficient neural network based indoor-outdoor scene classification algorithm. In: Int. Conf. on Consumer Electronics (ICCE). Digest of Technical Papers, pp. 317–318 (2010)Google Scholar
  22. 22.
    Torralba, A., Oliva, A.: Semantic organization of scenes using discriminant structural templates. In: Int. Conf. on Computer Vision, pp. 1253–1258 (1999)Google Scholar
  23. 23.
    Torralba, A., Oliva, A.: Depth estimation from image structure. IEEE Trans. on Pattern Analysis and Machine Intelligence 24 (2002)Google Scholar
  24. 24.
    Tversky, B., Hemenway, K.: Categories of environmental scenes. Cognitive Psychology 15(1), 121–149 (1983)CrossRefGoogle Scholar
  25. 25.
    Vailaya, A., Figueiredo, M., Jain, A., Zhang, H.J.: Image classification for content-based indexing. IEEE Trans. on Image Processing 10(1), 117–130 (2001)CrossRefMATHGoogle Scholar
  26. 26.
    Wei, Q.Q.: Converting 2d to 3d: A survey (2005)Google Scholar
  27. 27.
    Wu, J., Rehg, J.M.: Centrist: A visual descriptor for scene categorization. IEEE Trans. on Pattern Analysis and Machine Intelligence 99 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ignazio Pillai
    • 1
  • Riccardo Satta
    • 1
  • Giorgio Fumera
    • 1
  • Fabio Roli
    • 1
  1. 1.Deparment of Electrical and Electronic EngineeringUniv. of Cagliari Piazza d’ArmiCagliariItaly

Personalised recommendations