Efficient action localization with approximately normalized fisher vectors

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2014, Pages 2545-2552

  Oneata, Dan ^a   Verbeek, Jakob ^a   Schmid, Cordelia ^a  

^a UNIV GRENOBLE ALPES   (France)

Author keywords

action classification; action localization; branch and bound; Fisher vectors; normalizations; sliding window

Indexed keywords

BRANCH AND BOUND METHOD; IMAGE ENHANCEMENT;

ACTION CLASSIFICATIONS; ACTION LOCALIZATION; FISHER VECTORS; NORMALIZATIONS; SLIDING WINDOW;

PATTERN RECOGNITION;

EID: 84911423364     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2014.326     Document Type: Conference Paper

References (35)

1. B. Alexe, T. Deselares, and V. Ferrari. Measuring the object-ness of image windows. PAMI, 34(11):2189-2202, 2012.
2. S. An, P. Peursum, W. Liu, and S. Venkatesh. Efficient algorithms for subwindow search in object detection and localization. In CVPR, pages 264-271, 2009.
3. K. Chatfield, V. Lempitsky, A. Vedaldi, and A. Zisserman. The devil is in the details: an evaluation of recent feature encoding methods. In BMVC, 2011.
4. Q. Chen, Z. Song, R. Feris, A. Datta, L. Cao, Z. Huang, and S. Yan. Efficient maximum appearance search for large-scale object detection. In CVPR, 2013.
5. R. Cinbis, J. Verbeek, and C. Schmid. Image categorization using Fisher kernels of non-iid image models. In CVPR, 2012.
6. R. Cinbis, J. Verbeek, and C. Schmid. Segmentation driven object detection with Fisher vectors. In ICCV, 2013.
7. G. Csurka and F. Perronnin. An efficient approach to semantic segmentation. IJCV, 95(2):198-212, 2011.
8. M. V. den Bergh, G. Roig, X. Boix, S. Manen, and L. V. Gool. Online video seeds for temporal window objectness. In ICCV, 2013.
9. O. Duchenne, I. Laptev, J. Sivic, F. Bach, and J. Ponce. Automatic annotation of human actions in video. In ICCV, 2009.
10. A. Gaidon, Z. Harchaoui, and C. Schmid. Actom sequence models for efficient action detection. In CVPR, 2011.
11. H. Harzallah, F. Jurie, and C. Schmid. Combining efficient object localization and image classification. In ICCV, 2009.
12. T. Jaakkola and D. Haussler. Exploiting generative models in discriminative classifiers. In NIPS, 1999.
13. M. Jain, H. Jégou, and P. Bouthemy. Better exploiting motion for better action recognition. In CVPR, 2013.
14. H. Jégou, F. Perronnin, M. Douze, J. Sánchez, P. Pérez, and C. Schmid. Aggregating local image descriptors into compact codes. PAMI, 34(9):1704-1716, 2012.
15. H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre. HMDB: a large video database for human motion recognition. In ICCV, 2011.
16. C. Lampert, M. Blaschko, and T. Hofmann. Efficient subwindow search: a branch and bound framework for object localization. PAMI, 31(12):2129-2142, 2009.
17. I. Laptev and P. Pérez. Retrieving actions in movies. In ICCV, 2007.
18. S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In CVPR, 2006.
19. Z. Li, E. Gavves, K. van de Sande, C. Snoek, and A. Smeul-ders. Codemaps, segment classify and search objects locally. In ICCV, 2013.
20. M. Marszalek, I. Laptev, and C. Schmid. Actions in context. In CVPR, 2009.
21. D. Oneata, J. Verbeek, and C. Schmid. Action and event recognition with Fisher vectors on a compact feature set. In ICCV, 2013.
22. F. Perronnin and C. Dance. Fisher kernels on visual vocabularies for image categorization. In CVPR, 2007.
23. F. Perronnin, J. Sánchez, and T. Mensink. Improving the Fisher kernel for large-scale image classification. In ECCV, 2010.
24. J. Sánchez and F. Perronnin. High-dimensional signature compression for large-scale image classification. In CVPR, 2011.
25. J. Sánchez, F. Perronnin, T. Mensink, and J. Verbeek. Image classification with the Fisher vector: Theory and practice. IJCV, 105(3):222-245, 2013.
26. C. Schuldt, I. Laptev, and B. Caputo. Recognizing human actions: a local SVM approach. In ICPR, 2004.
27. C. Sun and R. Nevatia. Large-scale web video event classification by use of Fisher vectors. In WA C V, 2013.
28. J. Uijlings, K. van de Sande, T. Gevers, and A. Smeulders. Selective search for object recognition. IJCV, 104(2):154-171, 2013.
29. A. Vedaldi, V. Gulshan, M. Varma, and A. Zisserman. Multiple kernels for object detection. In ICCV, 2009.
30. P. Viola and M. Jones. Robust real-time object detection. IJCV, 57(2):137-154, 2004.
31. H. Wang, A. Kläser, C. Schmid, and C.-L. Liu. Dense trajectories and motion boundary descriptors for action recognition. IJCV, 103(1):60-79, 2013.
32. H. Wang and C. Schmid. Action recognition with improved trajectories. In ICCV, 2013.
33. X. Wang, L. Wang, and Y. Qiao. A comparative study of encoding, pooling and normalization methods for action recognition. In ACCV, 2012.
34. C. Xu, C. Xiong, and J. Corso. Streaming hierarchical video segmentation. In ECCV, 2012.
35. J. Yuan, Z. Liu, and Y. Wu. Discriminative subvolume search for efficient action detection. In CVPR, 2009.