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Scale Invariant Action Recognition Using Compound Features Mined from Dense Spatio-temporal Corners

  • Andrew Gilbert
  • John Illingworth
  • Richard Bowden
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5302)

Abstract

The use of sparse invariant features to recognise classes of actions or objects has become common in the literature. However, features are often ”engineered” to be both sparse and invariant to transformation and it is assumed that they provide the greatest discriminative information. To tackle activity recognition, we propose learning compound features that are assembled from simple 2D corners in both space and time. Each corner is encoded in relation to its neighbours and from an over complete set (in excess of 1 million possible features), compound features are extracted using data mining. The final classifier, consisting of sets of compound features, can then be applied to recognise and localise an activity in real-time while providing superior performance to other state-of-the-art approaches (including those based upon sparse feature detectors). Furthermore, the approach requires only weak supervision in the form of class labels for each training sequence. No ground truth position or temporal alignment is required during training.

Keywords

Association Rule Action Recognition Interest Point Frequent Itemset Mining Association Rule 
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.

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References

  1. 1.
    Schuldt, C., Laptev, I., Caputo, B.: Recognizing Human Actions: a Local SVM Approach. In: Proc. of International Conference on Pattern Recognition (ICPR 2004), vol. III, pp. 32–36 (2004)Google Scholar
  2. 2.
    Viola, P., Jones, M.: Rapid Object Detection using a Boosted Cascade of Simple Features. In: Proc. of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2001), vol. I, pp. 511–518 (2001)Google Scholar
  3. 3.
    Ke, Y., Sukthankar, R., Hebert, M.: Efficient Visual Event Detection using Volumetric Features. In: Proc. of IEEE International Conference on Computer Vision (ICCV 2005) (2005)Google Scholar
  4. 4.
    Cooper, H.M., Bowden, R.: Sign Language Recognition Using Boosted Volumetric Features. In: Proc. IAPR Conf. on Machine Vision Applications, pp. 359–362 (2007)Google Scholar
  5. 5.
    Dollar, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior Recognition via Sparse Spatio-temporal Features. In: ICCCN 2005: Proceedings of the 14th International Conference on Computer Communications and Networks, pp. 65–72 (2005)Google Scholar
  6. 6.
    Laptev, I., Pérez.: Retrieving Actions in Movies. In: Proc. of IEEE International Conference on Computer Vision (ICCV 2007) (2007)Google Scholar
  7. 7.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB 1994, Proceedings of 20th International Conference on Very Large Data Bases, pp. 487–499 (1994)Google Scholar
  8. 8.
    Quack, T., Ferrari, V., Leibe, B., Gool, L.: Efficient Mining of Frequent and Distinctive Feature Configurations. In: Proc. of IEEE International Conference on Computer Vision (ICCV 2007) (2007)Google Scholar
  9. 9.
    Lazebnik, S., Schmid, C., Ponce, J.: Semi-Local Affine Parts for Object Recognition. In: Proc. of BMVA British Machine Vision Conference (BMVC 2004), vol. II, pp. 959–968 (2004)Google Scholar
  10. 10.
    Sivic, J., Zisserman, A.: Video Data Mining using Configurations of Viewpoint Invariant Regions. In: Proc. of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2004), vol. I, pp. 488–495 (2004)Google Scholar
  11. 11.
    Niebles, J.C., Fei-Fei, L.: A Hierarchical Model of Shape and Appearance for Human Action Classification. In: Proc. of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2007) (2007)Google Scholar
  12. 12.
    Scovanner, P., Ali, S., Shah, M.: A 3-dimensional sift descriptor and its application to action recognition. In: Proc. of MULTIMEDIA 2007, pp. 357–360 (2007)Google Scholar
  13. 13.
    Lowe, D.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 20, 91–110 (2003)Google Scholar
  14. 14.
    Dalal, N., Triggs, B., Schmid, C.: Human Detection using Oriented Histograms of Flow and Apperance. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 428–441. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  15. 15.
    Lucas, B., Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision. In: Proc. of 7th International Joint Conference on Artificial Intelligence (IJCAI), pp. 674–679 (1998)Google Scholar
  16. 16.
    Song, Y., Goncalves, L., Perona, P.: Unsupervised Learning of Human Motion. Transactions on Pattern Analysis and Machine Intelligence 25, 814–827 (2003)CrossRefGoogle Scholar
  17. 17.
    Tesic, J., Newsam, S., Manjunath, B.S.: Mining image datasets using perceptual association rules. In: Proc. SIAM International Conference on Data Mining, Workshop on Mining Scientific and Engineering Datasets, pp. 71–77 (2003)Google Scholar
  18. 18.
    Ding, Q., Ding, Q., Perrizo, W.: Association rule mining on remotely sensed images using p-trees. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 66–79 (2002)Google Scholar
  19. 19.
    Chum, O., Philbin, J., Sivic, J., Isard, M., Zisserman, A.: Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval. In: Proc. IEEE International Conference on Computer Vision (ICCV 2007), pp. 1–8 (2007)Google Scholar
  20. 20.
    Harris, C., Stphens, M.: A Combined Corner and Edge Detector. In: Proc. of Alvey Vision Conference, 189–192 (1988)Google Scholar
  21. 21.
    Fleuret, F., Geman, D.: Coarse to Fine Face Detection. International Journal of Computer Vision 41, 85–107 (2001)CrossRefzbMATHGoogle Scholar
  22. 22.
    Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proc. of the 1993 ACM SIGMOD International Conference on Management of Data SIGMOD 1993, pp. 207–216 (1993)Google Scholar
  23. 23.
    Nowozin, S., Bakir, G., Tsuda, K.: Discriminative Subsequence Mining for Action Classification. In: Proc. of IEEE International Conference on Computer Vision (ICCV 2007), pp. 1919–1923 (2007)Google Scholar
  24. 24.
    Wong, S.F., Cipolla, R.: Extracting Spatio Temporal Interest Points using Global Information. In: Proc. of IEEE International Conference on Computer Vision (ICCV 2007) (2007)Google Scholar
  25. 25.
    Niebles, J., Wang, H., Fei-Fei, L.: Unsupervised Learning of Human Action Categories using Spatial-Temporal Words. In: Proc. of BMVA British Machine Vision Conference (BMVC 2006), vol. III, pp. 1249–1259 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Andrew Gilbert
    • 1
  • John Illingworth
    • 1
  • Richard Bowden
    • 1
  1. 1.CVSSPUniversity of SurreyGuildfordEngland

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