Canonical Correlation-Based Feature Fusion Approach for Scene Classification

  • J. Arunnehru
  • A. Yashwanth
  • Shaik Shammer
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 736)


Vision-based scene recognition and analysis is an emerging field and actively conceded in computer vision and robotics area. Classifying the complex scenes in a real-time environment is a challenging task to solve. In this paper, an indoor and outdoor scene recognition approach by linear combination (fusion) of global descriptor (GIST) and Local Energy based Shape Histogram (LESH) descriptor with Canonical Correlation Analysis (CCA) is proposed. The experiments have been carried out using publicly available 15-dataset and the fused features are modeled by Random forest and K-Nearest Neighbor for classification. In the experimental results, K-NN exhibits the good performance in our proposed approach with an average accuracy rate of 81.62%, which outperforms the random forest classifier.


Scene recognition Feature extraction Feature fusion K-Nearest Neighbor Random forest 


  1. 1.
    Quattoni, A., Torralba, A.: Recognizing indoor scenes. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2009), pp. 413–420. IEEE (2009)Google Scholar
  2. 2.
    Niu, Z., Hua, G., Gao, X., Tian, Q.: Context aware topic model for scene recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2743–2750. IEEE (2012)Google Scholar
  3. 3.
    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 (CVPR 2005), vol. 2, pp. 524–531. IEEE (2005)Google Scholar
  4. 4.
    Szummer, M., Picard, R.W.: Indoor-outdoor image classification. In: Proceedings of the IEEE International Workshop on Content-Based Access of Image and Video Database, pp. 42–51. IEEE (1998)Google Scholar
  5. 5.
    Luo, J., Savakis, A.: Indoor vs outdoor classification of consumer photographs using low-level and semantic features. In: Proceedings of the International Conference on Image Processing, vol. 2, pp. 745–748. IEEE (2001)Google Scholar
  6. 6.
    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
  7. 7.
    Li, Q., Wu, J., Tu, Z.: Harvesting mid-level visual concepts from large-scale internet images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 851–858 (2013)Google Scholar
  8. 8.
    Chen, Y., Pan, D., Pan, Y., Liu, S., Gu, A., Wang, M.: Indoor scene understanding via monocular RGB-D images. Inf. Sci. 320, 361–371 (2015)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Shahriari, M., Bergevin, R.: A two-stage outdoor-indoor scene classification framework: experimental study for the outdoor stage. In: International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8. IEEE (2016)Google Scholar
  10. 10.
    Ren, Y., Chen, C., Li, S., Kuo, C.C.J.: GAL: a global-attributes assisted labeling system for outdoor scenes. J. Vis. Commun. Image Represent. 42, 192–206 (2017)CrossRefGoogle Scholar
  11. 11.
    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
  12. 12.
    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, vol. 2, pp. 2169–2178. IEEE (2006)Google Scholar
  13. 13.
    Wajid, S.K., Hussain, A.: Local energy-based shape histogram feature extraction technique for breast cancer diagnosis. Expert Syst. Appl. 42(20), 6990–6999 (2015)CrossRefGoogle Scholar
  14. 14.
    Thompson, B.: Canonical correlation analysis. In: Encyclopedia of Statistics in Behavioral Science (2005)Google Scholar
  15. 15.
    Liaw, A., Wiener, M., et al.: Classification and regression by randomForest. R News 2(3), 18–22 (2002)Google Scholar
  16. 16.
    Wu, X., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Philip, S.Y., et al.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1), 1–37 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringSRM UniversityChennaiIndia

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