Précédent|Prochain

Scene classification of remote sensing images by optimizing visual vocabulary concerning scene label information

  • role： First author第一作者
  • Affiliation：

    Geodesy and Geomatics School, Wuhan University, Wuhan 430079, China

  • Email：lyan@whu.edu.cn
  • Introduction：闫利(1966—　)，男，教授，研究方向为摄影测量与遥感，地面三维激光扫描。E-mail：lyan@whu.edu.cn
YAN Li,  
  • Affiliation：

    Geodesy and Geomatics School, Wuhan University, Wuhan 430079, China

ZHU Ruixi,  
  • role： Corresponding author通信作者
  • Affiliation：

    Geodesy and Geomatics School, Wuhan University, Wuhan 430079, China

  • Email：yliu@sgg.whu.edu.cn
  • Introduction：刘异(1983—　)，女，副教授，研究方向为遥感图像分类、城市形态分析。E-mail：yliu@sgg.whu.edu.cn
LIU Yi*,  
  • Affiliation：

    Geodesy and Geomatics School, Wuhan University, Wuhan 430079, China

MO Nan

résumé

The traditional Bag Of Words (BOW) model disregards the scene label information of remote sensing images and ambiguity or redundancy of visual vocabularies. Hence, utilizing BOW to classify categories with similar backgrounds is unsuitable. Therefore, we propose an image scene classification algorithm based on the optimization of visual words with respect to scene label information to handle the said problem.This paper reports on an image scene classification algorithm based on the optimization of visual words with respect to scene label information. The algorithm procedure is as follows: first, images are divided into patches utilizing Spatial Pyramid Matching, and then Scale Invariant Features Transform (SIFT) features are extracted for each local image patch. These features are then clustered with K-means to form a histogram of each patch at different levels utilizing the Boiman strategy. We adopt Image Frequency as the feature selection method on visual words in each category to eliminate visual vocabulary irrelevant to a specific category and obtain a class-specific codebook. Principal Component Analysis (PCA) is then utilized to eliminate redundant visual vocabulary. Finally, we produce a mixture of class-specific histograms in each image patch at different pyramid levels and a traditional histogram with an adaptive weight. A fusion of histograms will be placed in a Support Vector Machine (SVM).We conducted experiments in this study on standard datasets of scene classification. Five experiments were conducted to demonstrate the performance of proposed algorithm. The first experiment shows that our algorithm performs better than methods that do not consider the scene label information with an increased accuracy of approximately 6 percent. The second experiment shows that the proposed method suitably performs in classifying categories with similar backgrounds and classifying error decreases in most categories. The third experiment demonstrates that the accuracy of the proposed method is higher at each pyramid level, and combined pyramids can offer even higher accuracy. The fourth experiment shows that method utilizing an adaptive weighted fusion method is more accurate than methods without. The final experiment demonstrates that the proposed algorithm performs better than other representative methods under the same conditions.This study proposes a method based on the optimization of visual words with respect to scene label information. This algorithm extracts SIFT features at different levels of pyramids combined with the Boiman strategy to generate universal histograms. DF is adopted as the feature selection method to remove visual words irrelevant to a specific category. PCA is then applied to remove redundancy and obtain class-specific codebook and histograms. Finally, a practical adaptive weighted fusion method that combines the traditional histograms of different levels with the class-specific histogram is proposed and placed in an SVM trainer and classifier. The experiment results show that the proposed algorithm suitably performs in classifying categories with similar backgrounds and displays higher stability. However, the proposed algorithm only considers one SIFT descriptor that corresponds to only one visual word. We can perform experiments on one SIFT descriptor that corresponds to several visual words and other feature selection procedures in future research.

mots-clés

scene classification;class-specific histogram;optimization of visual words;principal component analysis;image frequency;adaptive weighted mixture

References

  1. 1.
    Agarwal A and Triggs B. 2008.Multilevel image coding with hyperfeatures.International Journal of Computer Vision, 78(1): 15–27
  2. 2.
    Bo L F and Sminchisescu C. 2009.Efficient match kernel between sets of features for visual recognition//Advances in Neural Information Processing Systems. Vancouver, British Columbia, Canada: Curran Associates, Inc.:135–143
  3. 3.
    Boiman O, Shechtman E and Irani M. 2008.In defense of nearest-neighbor based image classification//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, AK: IEEE: 1–8
  4. 4.
    Cao L L and Li F F. 2007. Spatially coherent latent topic model for concurrent segmentation and classification of objects and scenes// Proceedings of the IEEE 11th International Conference on Computer Vision. Rio de Janeiro:IEEE: 1–8
  5. 5.
    Chen J N, Huang H K, Tian S F and Qu Y L. 2009.Feature selection for text classification with Naïve Bayes. Expert Systems with Applications, 36(3): 5432–5435
  6. 6.
    Csurka G, Dance C R, Fan L Z, Willamowski J and Bray C. 2004. Visual categorization with bags of keypoints//Workshop on Statistical Learning in Computer Vision.Prague:ECCV: 1–22
  7. 7.
    Dalal N and Triggs B. 2005.Histograms of oriented gradients for human detection//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05). San Diego, CA, USA:IEEE: 886–893
  8. 8.
    Datta R, Joshi D, Li J and Wang J Z. 2008.Image retrieval: ideas, influences, and trends of the new age. ACM Computing Surveys, 40(2):Article No. 5
  9. 9.
    Hao J and Jie X. 2010.Improved bags-of-words algorithm for scene recognition// Proceedings of the 2010 2nd International Conference on Signal Processing Systems (ICSPS). Dalian:IEEE: V2-279-V2-282
  10. 10.
    Jiang Y, Wang R S and Wang C. 2010.Scene classification with context pyramid features. Journal of Computer-Aided Design and Computer Graphics, 22(8): 1366–1373
  11. 11.
    Jiang Y G, Ngo C W and Yang J. 2007.Towards optimal bag-of-features for object categorization and semantic video retrieval//Proceedings of the 6th ACM International Conference on Image and Video Retrieval. Amsterdam, The Netherlands:ACM: 494–501
  12. 12.
    Juneja M, Vedaldi A, Jawahar C V and Zisserman A. 2013.Blocks that shout: distinctive parts for scene classification//Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition.Portland, OR: IEEE: 923–930
  13. 13.
    Jurie F and Triggs B. 2005.Creating efficient codebooks for visual recognition// Proceedings of the 10th IEEE International Conference on Computer Vision. Beijing, China: IEEE: 604–610
  14. 14.
    Karpathy A, Toderici G, Shetty S, Leung T, Sukthankar R and Li F F. 2014. Large-scale video classification with convolutional neural networks//Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. Columbus, OH: IEEE: 1725–1732
  15. 15.
    Krizhevsky A, Sutskever I and Hinton G E. 2012.Imagenet classification with deep convolutional neural networks//Advances in Neural Information Processing Systems. Lake Tahoe, Nevada:Curran Associates Inc.: 1097–1105
  16. 16.
    Larlus D and Jurie F. 2006.Latent mixture vocabularies for object categorization// Proceedings of the 17th British Machine Vision Conference.BMVA Press: 959–968
  17. 17.
    Li Q, Zhang H G, Guo J, Bhanu B and An L. 2012. Improving bag-of-words scheme for scene categorization. The Journal of China Universities of Posts and Telecommunications, 19: 166–171
  18. 18.
    Moosmann F, Nowak E and Jurie F. 2008.Randomized clustering forests for image classification.IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(9): 1632–1646
  19. 19.
    Oliva A and Torralba A. 2001.Modeling the shape of the scene: A holistic representation of the spatial envelope.International Journal of Computer Vision, 42(3): 145–175
  20. 20.
    Perronnin F. 2008. Universal and adapted vocabularies for generic visual categorization.IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(7): 1243–1256
  21. 21.
    Sadeghi F and Tappen M F. 2012.Latent pyramidal regions for recognizing scenes//Proceedings of the 12th European Conference on Computer Vision. Berlin Heidelberg:Springer: 228–241
  22. 22.
    Singh S, Gupta A and Efros A A. 2012.Unsupervised discovery of mid-level discriminative patches// Proceedings of the 12th European Conference on Computer Vision.Berlin Heidelberg:Springer: 73–86
  23. 23.
    Sivic J and Zisserman A. 2003.Video Google: atext retrieval approach to object matching in videos// Proceedings of the 9th IEEE International Conference on Computer Vision. Nice, France: IEEE: 1470–1477
  24. 24.
    Van Gemert J C, Veenman C J, Smeulders A W M and Geusebroek J M. 2010. Visual word ambiguity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(7): 1271–1283
  25. 25.
    Wu J X and Rehg J M. 2011.CENTRIST: avisual descriptor for scene categorization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(8): 1489–1501
  26. 26.
    Xie W J. 2011. Research on Middle Semantic Representation Based Image Scene Classification.Beijing:BeijingJiaotong University
  27. 27.
    Yang J C, Yu K, Gong Y H and Huang T. 2009.Linear spatial pyramid matching using sparse coding for image classification// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Miami, FL:IEEE: 1794–1801
  28. 28.
    Yang Y and Newsam S. 2010. Bag-of-visual-words and spatial extensions for land-use classification//Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. San Jose, California:ACM: 270–279
  29. 29.
    Yang Y M and Pedersen J O. 1997.A comparative study on feature selection in text categorization//Proceedings of the Fourteenth International Conference on Machine Learning.San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.: 412–420
  30. 30.
    Zhao L J, Tang P, Huo L Z and Zheng K. 2014.Review of the bag-of-visual-words models in image scene classification.Journal of Image and Graphics, 19(3):333–343

Lire l'article complet

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website