Hierarchical System for Content Based Categorization and Orientation of Consumer Images

  • Gaurav Sharma
  • Abhinav Dhall
  • Santanu Chaudhury
  • Rajen Bhatt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)


A hierarchical framework to perform automatic categorization and reorientation of consumer images based on their content is presented. Sometimes the consumer rotates the camera while taking the photographs but the user has to later correct the orientation manually. The present system works in such cases; it first categorizes consumer images in a rotation invariant fashion and then detects their correct orientation. It is designed to be fast, using only low level color and edge features. A recently proposed information theoretic feature selection method is used to find most discriminant subset of features and also to reduce the dimension of feature space. Learning methods are used to categorize and detect the correct orientation of consumer images. Results are presented on a collection of about 7000 consumer images, collected by an independent testing team, from the internet and personal image collections.


Support Vector Machine Gaussian Mixture Model Correct Orientation Orientation Detection Spatial Pyramid Match 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Baluja, S.: Automated image-orientation detection: a scalable boosting approach. Pattern Analysis and Applications 10, 247–263 (2007)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Baluja, S., Rowley, H.A.: Large scale performance measurement of content-based automated image-orientation detection. In: ICIP (2003)Google Scholar
  3. 3.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001),
  4. 4.
    Fei-Fei, L., Perona, P.: A bayesian hierarchical model for learning natural scene categories. In: CVPR (2005)Google Scholar
  5. 5.
    Huang, J., Kumar, S.R., Mitra, M., Zhu, W.-J., Zabih, R.: Image indexing using color correlograms. In: CVPR (1997)Google Scholar
  6. 6.
    Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR (2006)Google Scholar
  7. 7.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)CrossRefGoogle Scholar
  8. 8.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. IJCV 42 (2001)Google Scholar
  9. 9.
    Scholkopf, B., Smola, A.J.: Learning with kernels. Support Vector Machines, Regularization, Optimization, and Beyond. The MIT Press, Cambridge (2001)Google Scholar
  10. 10.
    Vailaya, A., Zhang, H.-J., Yang, C., Liu, F.-I., Jain, A.K.: Automatic image orientation detection. IEEE Trans. IP 11(7), 746–755 (2002)Google Scholar
  11. 11.
    Vasconcelos, M., Vasconcelos, N.: Natural image statistics and low-complexity feature selection. PAMI 31(2), 228–244 (2009)MathSciNetGoogle Scholar
  12. 12.
    Wang, Y., Zhang, H.: Content-based image orientation detection with support vector machines. In: CBAIVL (2001)Google Scholar
  13. 13.
    Wang, Y.M., Zhang, H.: Detecting image orientation based on low-level visual content. CVIU 93, 328–346 (2004)Google Scholar
  14. 14.
    Willamowski, J., Arregui, D., Csurka, G., Dance, C.R., Fan, L.: Categorizing nine visual classes using local appearance descriptors. In: IWLAVS (2004)Google Scholar
  15. 15.
    Zhang, L., Li, M., Zhang, H.-J.: Boosting image orientation detection with indoor vs. outdoor classification. In: WACV (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Gaurav Sharma
    • 1
  • Abhinav Dhall
    • 1
  • Santanu Chaudhury
    • 2
  • Rajen Bhatt
    • 1
  1. 1.Samsung Delhi R&DNoida
  2. 2.Multimedia Lab, Dept. EEIndian Institute of Technology Delhi 

Personalised recommendations