On recognizing actions in still images via multiple features

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7585 LNCS, Issue PART 3, 2012, Pages 263-272

  Sener, Fadime ^a   Bas, Cagdas ^b   Ikizler Cinbis, Nazli ^b  

^a BILKENT UNIVERSITY   (Turkey)

^b HACETTEPE UNIVERSITY   (Turkey)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; LEARNING SYSTEMS; STATISTICAL TESTS;

HUMAN ACTIONS; LEARNING APPROACH; MULTI CUES; MULTIPLE FEATURES; MULTIPLE-INSTANCE LEARNING; OBJECT DETECTORS; OBJECT REGION; PERFORMANCE GAIN; STANFORD; STILL-IMAGES;

OBJECT DETECTION;

EID: 84867703790     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33885-4_27     Document Type: Conference Paper

References (21)

1. Yao, B., Jiang, X., Khosla, A., Lin, A.L., Guibas, L.J., Fei-Fei, L.: Human action recognition by learning bases of action attributes and parts. In: International Conference on Computer Vision (ICCV), Barcelona, Spain (November 2011)
2. Gupta, A., Kembhavi, A., Davis, L.S.: Observing human-object interactions: Using spatial and functional compatibility for recognition. TPAMI 31, 1775-1789 (2009)
3. Yao, B., Fei-Fei, L.: Modeling mutual context of object and human pose in human-object interaction activities. In: CVPR, San Francisco, CA (June 2010)
4. Prest, A., Schmid, C., Ferrari, V.: Weakly supervised learning of interactions between humans and objects. IEEE TPAMI 34, 601-614 (2012)
5. Alexe, B., Deselaers, T., Ferrari, V.: What is an object? In: IEEE Conf. on Computer Vision and Pattern Recognition, San Francisco, USA (2010)
6. Poppe, R.: A survey on vision-based human action recognition. Image Vision Computing 28, 976-990 (2010)
7. Weinland, D., Ronfard, R., Boyer, E.: A survey of vision-based methods for action representation, segmentation and recognition. CVIU 115, 224-241 (2011)
8. Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: CVPR (2008)
9. Wang, Y., Jiang, H., Drew, M.S., Li, Z.N., Mori, G.: Unsupervised discovery of action classes. In: CVPR (2006)
10. Thurau, C., Hlavac, V.: Pose primitive based human action recognition in videos or still images. In: CVPR (2008)
11. Ikizler-Cinbis, N., Cinbis, R.G., Sclaroff, S.: Learning actions from the web. In: Int. Conf. on Computer Vision (2009)
12. Yao, B., Fei-Fei, L.: Grouplet: a structured image representation for recognizing human and object interactions. In: The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA (June 2010)
13. Desai, C., Ramanan, D., Fowlkes, C.: Discriminative models for static humanobject interactions. In:Workshop on Structured Models in Computer Vision (2010)
14. Delaitre, V., Sivic, J., Laptev, I.: Learning person-object interactions for action recognition in still images. In: NIPS (2011)
15. Delaitre, V., Laptev, I., Sivic, J.: Recognizing human actions in still images: a study of bag-of-features and part-based representations. In: BMVC (2010)
16. Yao, B., Khosla, A., Fei-Fei, L.: Combining randomization and discrimination for fine-grained image categorization. In: CVPR, Springs, USA (June 2011)
17. Chen, Y., Bi, J., Wang, J.Z.: Miles: Multiple-instance learning via embedded instance selection. IEEE TPAMI 28, 1931-1947 (2006)
18. Patron-Perez, A., Marszalek, M., Reid, I., Zisserman, A.: High five: Recognising human interactions in tv shows. In: British Machine Vision Conference (2010)
19. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR (2001)
20. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60, 91-110 (2004)
21. Bourdev, L., Malik, J.: Poselets: Body part detectors trained using 3D human pose annotations. In: ICCV (2009)