Journal of Signal Processing Systems

, Volume 61, Issue 3, pp 251–258 | Cite as

A Novel Method for Efficient Indoor–Outdoor Image Classification

  • Wonjun Kim
  • Jimin Park
  • Changick Kim


Most traditional indoor–outdoor scene classification approaches utilize the simple statistics of the low-level features, such as colors, edges, and textures. However, the existence of colors similar to sky or grass often yields the false positives. To cope with this deficiency, we focus on the orientation of low-level features in this paper. First, the image is partitioned into five block regions, whose features are differently weighted in the following classification stage according to the block positions. The edge and color orientation histogram (ECOH) descriptors are defined to represent each block efficiently. Finally, all ECOH values are concatenated to generate the feature vector and fed into the SVM classifier for the indoor–outdoor scene classification. To justify the efficiency and robustness of the proposed method, the evaluation is conducted over 1200 images.


Indoor–outdoor classification Low-level features Edge and color orientation histogram SVM classifier 



This research was supported by the MKE(The Ministry of Knowledge Economy), Korea, under the ITRC(Information Technology Research Center) support program supervised by the NIPA(National IT Industry Promotion Agency NIPA-2010-(C1090-0902-0017)).


  1. 1.
    Zhang, L., Mingjing, L., Hong-Jing, Z. (2002). Boosting image orientation detection with indoor vs. outdoor classification. IEEE Workshop on Applications of Computer Vision (WACV 2002), Orlando, FL, pp. 95–99.Google Scholar
  2. 2.
    Bianco, S., Ciocca, G., Cusano, C., & Schettini, R. (2008). Improving color constancy using indoor–outdoor image classification. IEEE Transactions on Image Processing, 17(12), 2381–2392.CrossRefMathSciNetGoogle Scholar
  3. 3.
    Szummer, M., Picard, R., W. (1998). Indoor–outdoor image classification. IEEE Workshop on Content-Based Access of Image and Video Database, Bombay, India, pp. 42–51.Google Scholar
  4. 4.
    Ohta, Y., Kanade, T., & Sakai, T. (1980). Color information for region segmentation. Computer Graphics and Image Processing, 13, 222–241.CrossRefGoogle Scholar
  5. 5.
    Serrano, N., Savakis, A., Luo, J. (2002). A computationally efficient approach to indoor/outdoor scene classification. International Conference on Pattern Recognition (ICPR), QC, Canada, 4, pp. 146–149.Google Scholar
  6. 6.
    Gupta, L., et al. (2007). Indoor versus outdoor scene classification using probabilistic neural network. EURASIP Journal on Advances in Signal Processing, 1, 123–133.Google Scholar
  7. 7.
    Payne, A., & Singh, S. (2005). Indoor vs. outdoor scene classification in digital photographs. Pattern Recognition, 38(10), 1533–1545.CrossRefGoogle Scholar
  8. 8.
    Luo, J., Savakis, A. (2001). Indoor vs. outdoor classification of consumer photographs using low-level and semantic features. International Conference on Image Processing (ICIP), Thessaloniki, Greece, vol. 2, pp. 745–748.Google Scholar
  9. 9.
    Boutell, M., Luo, J. (2004). Bayesian fusion of camera metadata cues in semantic scene classification. Computer Vision and Pattern Recognition (CVPR), Washington, D.C., vol. 2, pp. 623–630.Google Scholar
  10. 10.
    Boutell, M., Choudhury, A., Luo, J., Brown, C., M. (2006). Using semantic features for scene classification: how good do they need to be ?. International Conference on Multimedia and Expo (ICME), Toronto, Canada, pp. 785–788.Google Scholar
  11. 11.
    Vailaya, A., Figueiredo, M., Jain, A. K., & Zhang, H.-J. (2001). Image classification for content-based indexing. IEEE Transactions on Image Processing, 10(1), 117–130.zbMATHCrossRefGoogle Scholar
  12. 12.
    Spyrou, E., Mylonas, P., Avrithis, Y. (2008). Using region semantics and visual context for scene classification. International Conference on Image Processing (ICIP), San Diego, CA, pp. 53–56.Google Scholar
  13. 13.
    Kim, W., & Kim, C. (2009). An efficient indoor-outdoor scene classification method. Journal of the Institute of Electronics Engineers of Korea, 46-SP(5), 48–55.Google Scholar
  14. 14.
    Burges, C. J. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2, 121–167.CrossRefGoogle Scholar
  15. 15.
    Chen, Y.-T., & Chen, C.-S. (2008). Fast human detection using a novel boosted cascading structure with meta stages. IEEE Transactions on Image Processing, 17(8), 1452–1464.CrossRefMathSciNetGoogle Scholar
  16. 16.
    Dalal, N., Triggs, B. (2005). Histograms of oriented gradients for human detection. Computer Vision and Pattern Recognition (CVPR), San Diego, CA, vol. 1, pp. 886–893.Google Scholar
  17. 17.
    Gonzalez, R. C., & Woods, R. E. (2002). Digital image processing. Upper Saddle River: Prentice-Hall.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)Yuseong-guSouth Korea

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