Journal of the Institute of Electronics Engineers of Korea SP (대한전자공학회논문지SP)

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지

An Efficient Indoor-Outdoor Scene Classification Method

효율적인 실내의 영상 분류 기법

  • 김원준 (한국과학기술원 전기및전자공학과) ;
  • 김창익 (한국과학기술원 전기및전자공학과)
  • Published : 2009.09.25
Download PDF
⟨ Previous Next ⟩

Abstract

Prior research works in indoor-outdoor classification have been conducted based on a simple combination of low-level features. However, since there are many challenging problems due to the extreme variability of the scene contents, most methods proposed recently tend to combine the low-level features with high-level information such as the presence of trees and sky. To extract these regions from videos, we need to conduct additional tasks, which may yield the increasing number of feature dimensions or computational burden. Therefore, an efficient indoor-outdoor scene classification method is proposed in this paper. First, the video is divided into the five same-sized blocks. Then we define and use the edge and color orientation histogram (ECOH) descriptors to represent each sub-block efficiently. Finally, all ECOH values are simply concatenated to generated the feature vector. To justify the efficiency and robustness of the proposed method, a diverse database of over 1200 videos is evaluated. Moreover, we improve the classification performance by using different weight values determined through the learning process.

실내외 영상 분류에 대한 연구는 밝기나 에지 정보와 같이 하위 레벨(low-level) 정보의 단순 결합을 이용하여 수행되어 왔다. 그러나 기존의 하위 레벨 영상 정보만을 기반으로 하는 실내외 영상 분류 방법은 다양한 콘텐츠를 극복하는데 한차가 군기 때문에 상위 레벨(high-level) 영상 정보를 함께 이용하는 방법에 대한 연구가 많이 진행되어 왔다. 이러한 연구의 대부분은 영상 내 하늘이나 수풀과 같은 영역을 검출하기 위해 별도의 알고리즘을 수행하기 때문에 특징 벡터의 차원을 증가시키거나 수행 속도를 저하시키는 문제점이 있다. 따라서 본 논문에서는 이러한 문제점을 극복하기 위해 효율적인 실내외 영상 분류 기법을 제안한다. 먼저 효과적인 특징 벡터를 생성하기 위해 영상을 5개의 하위 블록으로 나눈다. 각각의 블록에 대하여, 제안하는 에지 색상 방향 히스토그램(edge and color orientation histogram, ECOH) 기술사(descriptor)를 이용하여 해당 블록을 표현하고 모든 블록의 값을 연결하여 최종적으로 특징 벡터를 생성한다. 제안하는 알고리즘의 효율성과 강건함을 보이기 위해 1200개 이상의 다양한 실내외 영상을 사용하였으며, 학습을 통해 각 영역의 가중치를 결정하여 분류 성능을 향상 시켰다.

Keywords

References

  1. A. C. Loui and A. E. Savakis, "Automatic image event segmentation and quality screening for albuming applications," International Conference on Multimedia and Expo, vol. 2, pp. 1125-1128, Aug. 2000
  2. N. Serrano, A. Savakis, and J. Luo, "A computationally efficient approach to indoor/ outdoor scene classification," International Conference on Pattern Recognition, vol. 4, pp. 146-149, 2002 https://doi.org/10.1109/ICPR.2002.1047420
  3. L. Gupta et al., "Indoor versus outdoor scene classification using probabilistic neural network," EURASIP Journal on Advances in Signal Processing, no. 1, pp. 123-133, Jan. 2007 https://doi.org/10.1155/2007/94298
  4. S. Bianco, G. Ciocca, C. Cusano, and R. Schettini, "Improving color constancy using indoor-outdoor image classification," IEEE Transactions on Image Processing, vol. 17, no. 12, pp. 2381-2392, Dec. 2008 https://doi.org/10.1109/TIP.2008.2006661
  5. M. Szummer and R. W. Picard, "Indoor-outdoor image classification," IEEE International Workshop on Content-Based Access of Image and Video Database, pp. 42-51, Jan. 1998
  6. Y. I. Ohta, T. Kanade, and T. Sakai, "Color information for region segmentation," Computer Graphics and Image Processing, no. 13, pp. 222-241, 1980
  7. J. Luo and A. Savakis, "Indoor vs outdoor classification of consumer photographs using low-level and semantic features," International Conference on Image Processing, vol. 2, pp. 745-748, Oct. 2001
  8. M. Boutell and J. Luo, "Bayesian fusion of camera metadata cues in semantic scene classification," International Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 623-630, July 2004
  9. A. Payne and S. Singh, "Indoor vs. outdoor scene classification in digital photographs," Pattern Recognition, vol. 38, no. 10, pp. 1533-1545, 2005 https://doi.org/10.1016/j.patcog.2004.12.014
  10. Y-T Chen and C-S Chen, "Fast human detection using a novel boosted cascading structure with meta stages," IEEE Transactions on Image Processing, vol. 17, no. 8, pp. 1452-1464, Aug. 2008 https://doi.org/10.1109/TIP.2008.926152
  11. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," International Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886-893, June 2005
  12. R. C. Gonzalez and R. E. Woods, Digital Image Processing, Second ed., Upper Saddle River, NJ: Prentice-Hall, 2002
  13. C. J. C Burges, "A Tutorial on Support Vector Machines for Pattern Recognition," Data Mining and Knowledge Discovery, vol.2, pp.121-167, 1998. https://doi.org/10.1023/A:1009715923555