Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Bridging Human-Centered Social Media Content Across Web Domains Read chapter Read first chapter

2014 | OriginalPaper | Chapter

5. Integrating Randomization and Discrimination for Classifying Human-Object Interaction Activities

Authors : Aditya Khosla, Bangpeng Yao, Li Fei-Fei

Published in: Human-Centered Social Media Analytics

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In this chapter we study the problem of classifying human–object interaction activities in still images. The goal of our method is to explore fine image statistics and identify the discriminative image patches for recognition. We achieve this goal by combining two ideas, discriminative feature mining and randomization. Discriminative feature mining allows us to model the detailed information that distinguishes different classes of images, while randomization allows us to handle the huge feature space and prevent over-fitting. We propose a random forest with discriminative decision trees algorithm where every tree node is a discriminative classifier that is trained by combining the information in this node as well as all upstream nodes. Besides human action recognition in still images, we also evaluate our method on subordinate categorization. Experimental results show that our method identifies semantically meaningful visual information and outperforms state-of-the-art algorithms on various datasets. Using our method, we achieved the best results and won the award in PASCAL VOC action classification challenges in 2011 and 2012.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Social Role Recognition for Human Event Understanding
next chapter Recognizing People in Social Context
Footnotes
1
We use the terms “patches” and “regions” interchangeably throughout this chapter.
 
2
A dictionary size of 1024, 256, 256 is used for PASCAL action [11, 12], PPMI [33], and Caltech-UCSD Birds [32] datasets respectively.
 
3
The baseline results are available from the dataset website: http://​ai.​stanford.​edu/​~bangpeng/​ppmi
 
6
These approaches were specifically developed for the 2012 PASCAL VOC challenge and have not been tested on other datasets but we expect similar performance improvements on them.
 
Literature
1.
Bernard, S., Heutte, L., Adam, S.: On the selection of decision trees in random forests. In: IEEE International Joint Conference on Neural Networks, IJCNN, pp. 302–307 (2009)
2.
Bosch, A., Zisserman, A., Munoz, X.: Image classification using random forests and ferns. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2007)
3.
Branson, S., Wah, C., Babenko, B., Schroff, F., Welinder, P., Perona, P., Belongie, S.: Visual recognition with humans in the loop. In: Proceedings of the European Conference on Computer Vision (ECCV) (2010)
4.
5.
Collin, C.A., McMullen, P.A.: Subordinate-level categorization relies on high spatial frequencies to a greater degree than basic-level categorization. Percept. Psychophys. 67(2), 354–364 (2005)CrossRef
6.
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2005)
7.
Delaitre, V., Laptev, I., Sivic, J.: Recognizing human actions in still images: a study of bag-of-features and part-based representations. In: Proceedings of the British Machine Vision Conference (BMVC) (2010)
8.
Dietterich, T.G.: An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach. Learn. 40, 139–157 (2000)CrossRef
9.
Duan, G., Huang, C., Ai, H., Lao, S.: Boosting associated pairing comparison features for pedestrian detection. In: Proceedings of the Workshop on Visual Surveillance (2009)
10.
Everingham, M., Van Gool, L., Williams, C., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2010 (VOC2010) Results (2010)
11.
Everingham, M., Van Gool, L., Williams, C., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2011 (VOC2011) Results (2011)
12.
Everingham, M., Van Gool, L., Williams, C., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2011 (VOC2012) Results (2012)
13.
Fei-Fei, L., Perona, P.: A Bayesian hierarchical model for learning natural scene categories. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2005)
14.
Fei-Fei, L., Fergus, R., Torralba, A.: Recognizing and learning object categories. Short Course in the IEEE International Conference on Computer Vision (2009)
15.
Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminantly trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1627–1645 (2010)CrossRef
16.
Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2003)
17.
Hillel, A.B., Weinshall, D.: Subordinate class recognition using relational object models. In: Proceedings of the Conference on Neural Information Processing Systems (NIPS) (2007)
18.
Johnson, K.E., Eilers, A.T.: Effects of knowledge and development on subordinate level categorization. Cogn. Dev. 13(4), 515–545 (1998)CrossRef
19.
Khosla, A., Xiao, J., Torralba, A., Oliva, A.: Memorability of image regions. In: Advances in Neural Information Processing Systems (NIPS), Lake Tahoe (2012)
20.
Khosla, A., Yao, B., Jayadevaprakash, N., Fei-Fei, L.: Novel dataset for fine-grained image categorization. In: First Workshop on Fine-Grained Visual Categorization, IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs (2011)
21.
Lazebnik, S.: Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2006)
22.
Li, L.-J., Su, H., Xing, E., Fei-Fei, L.: Object bank: a high-level image representation for scene classification and semantic feature sparsification. In: Proceedings of the Conference on Neural Information Processing Systems (NIPS) (2010)
23.
Lowe, David G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)CrossRef
24.
Moosmann, F., Triggs, B., Jurie, F.: Fast discriminative visual codebooks using randomized clustering forests. In: Proceedings of the Conference on Neural Information Processing Systems (NIPS) (2007)
25.
Ojala, T., Pietikainen, M., Harwood, D.: Performance evaluation of texture measures with classification based on kullback discrimination of distributions. In: Proceedings of the IEEE International Conference on Pattern Recognition (ICPR) (1994)
26.
Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the shape envelope. Int. J. Comput. Vision 42(3), 145–175 (2001)CrossRefMATH
27.
Shotton, J., Johnson, M., Cipolla, R.: Semantic texton forests for image categorization and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2008)
28.
Tu, Z.: Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2005)
29.
van de Sande, K.E.A., Gevers, T., Snoek, C.G.M.: Evaluating color descriptors for object and scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1582–1596 (2010)CrossRef
30.
van de Weijer, J., Schmid, C., Verbeek, J., Larlus, D.: Learning color names for real-world applications. IEEE Trans. Image Process. 18(7), 1512–1523 (2009)CrossRefMathSciNet
31.
Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2010)
32.
Welinder, P., Branson, S., Mita, T., Wah, C., Schroff, F., Belongie, S., Perona, P.: Caltech-UCSD birds 200. Technical Report CNS-TR-201, Caltech (2010)
33.
Yao, B., Fei-Fei, L.: Grouplet: a structured image representation for recognizing human and object interactions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2010)
34.
Yao, B., Fei-Fei, L.: Modeling mutual context of object and human pose in human-object interaction activities. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2010)
35.
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: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2011)
36.
Yao, B., Khosla, A., Fei-Fei, L.: Classifying actions and measuring action similarity by modeling the mutual context of objects and human poses. In: Proceedings of the International Conference on Machine Learning (ICML) (2011)
37.
Yao, B., Khosla, A., Fei-Fei, L.: Combining randomization and discrimination for fine-grained image categorization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2011)
38.
Yao, B., Bradski, G., Fei-Fei, L.: A codebook-free and annotation-free approach for fine-grained image categorization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2012)
39.
Yao, B., Fei-Fei, L.: Action recognition with exemplar based 2.5D graph matching. In: Proceedings of the European Conference on Computer Vision (ECCV) (2012)
Metadata
Title
Integrating Randomization and Discrimination for Classifying Human-Object Interaction Activities
Authors
Aditya Khosla
Bangpeng Yao
Li Fei-Fei
Copyright Year
2014
Book
Human-Centered Social Media Analytics

Print ISBN: 978-3-319-05490-2

Electronic ISBN: 978-3-319-05491-9

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-05491-9_5

Premium Partner

    Image Credits