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:
Image Annotation Using a Semantic Hierarchy Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Bags of Graphs for Human Action Recognition

Authors : Xavier Cortés, Donatello Conte, Hubert Cardot

Published in: Structural, Syntactic, and Statistical Pattern Recognition

Publisher: Springer International Publishing

Log in

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

search-config
loading …

Abstract

Bags of visual words are a well known approach for images classification that also has been used in human action recognition. This model proposes to represent images or videos in a structure referred to as bag of visual words before classifying. The process of representing a video in a bag of visual words is known as the encoding process and is based on mapping the interest points detected in the scene into the new structure by means of a codebook. In this paper we propose to improve the representativeness of this model including the structural relations between the interest points using graph sequences. The proposed model achieves very competitive results for human action recognition and could also be applied to solve graph sequences classification problems.

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 An Efficient Method for Boundary Detection from Hyperspectral Imagery
next chapter Categorization of RNA Molecules Using Graph Methods
Literature
1.
Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV, Prague, vol. 1, no. 1–22, pp. 1–2 (2004)
2.
Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 1794–1801. IEEE (2009)
3.
Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality constrained linear coding for image classification. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3360–3367. IEEE (2010)
4.
Niebles, J.C., Wang, H., Fei-Fei, L.: Unsupervised learning of human action categories using spatial-temporal words. Int. J. Comput. Vis. 79(3), 299–318 (2008)CrossRef
5.
Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)
6.
7.
Elshourbagy, M., Hemayed, E., Fayek, M.: Enhanced bag of words using multilevel k-means for human activity recognition. Egypt. Inform. J. 17(2), 227–237 (2016)CrossRef
8.
Mahé, P., Vert, J.-P.: Graph kernels based on tree patterns for molecules. Mach. Learn. 75(1), 3–35 (2009)CrossRef
9.
Qi, X., Wu, Q., Zhang, Y., Fuller, E., Zhang, C.-Q.: A novel model for DNA sequence similarity analysis based on graph theory. Evol. Bioinform. 7, 149–158 (2011)CrossRef
10.
Conte, D., Foggia, P., Sansone, C., Vento, M.: Thirty years of graph matching in pattern recognition. Int. J. Pattern Recogn. Artif. Intell. 18(3), 265–298 (2004)CrossRef
11.
Li, T., Dong, H., Shi, Y., Dehmer, M.: A comparative analysis of new graph distance measures and graph edit distance. Inf. Sci. 403–404, 15–21 (2017)CrossRef
12.
Solé-Ribalta, A., Cortés, X., Serratosa, F.: A Comparison between structural and embedding methods for graph classification. SSPR/SPR 2012, 234–242 (2012)
13.
Sanfeliu, A., Fu, K.: A distance measure between attributed relational graphs for pattern recognition. IEEE Trans. Syst. Man Cybern. 13, 353–362 (1983)CrossRef
14.
Bunke, H., Allermann, G.: Inexact graph matching for structural pattern recognition. Pattern Recogn. Lett. 1(4), 245–253 (1983)CrossRef
15.
Riesen, K., Bunke, H.: Approximate graph edit distance computation by means of bipartite graph matching. Image Vis. Comput. 27(4), 950–959 (2009)CrossRef
16.
Serratosa, F.: Speeding up fast bipartite graph matching through a new cost matrix. Int. J. Pattern Recogn. Artif. Intell. 29(2), 1550010 (2015)MathSciNetCrossRef
17.
Fischer, A., Suen, C.Y., Frinken, V., Riesen, K., Bunke, H.: Approximation of graph edit distance based on Hausdorff matching. Pattern Recogn. 48(2), 331–343 (2015)CrossRef
18.
19.
Silva, F.B., Werneck, R.d.O., Goldenstein, S., Tabbone, S., Torres, R.d.S.: Graph-based bag-of-words for classification. Pattern Recogn. 74, 266–285 (2018)CrossRef
20.
Laptev, I.: On space-time interest points. Int. J. Comput. Vis. 64(2–3), 107–123 (2005)CrossRef
21.
Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, Manchester, UK, vol. 15, no. 50, pp. 147–151 (1988)
22.
Andrew, A.M.: Another efficient algorithm for convex hulls in two dimensions. Inf. Process. Lett. 9(5), 216–219 (1979)CrossRef
23.
Pers, J., Sulic, V., Kristan, M., Perse, M., Polanec, K., Kovacic, S.: Histograms of optical flow for efficient representation of body motion. Pattern Recogn. Lett. 31(11), 1369–1376 (2010)CrossRef
24.
Hartigan, J.A., Wong, M.A.: Algorithm AS 136: a k-means clustering algorithm. J. Roy. Stat. Soc. Ser. C (Appl. Stat.) 28(1), 100–108 (1979)MATH
25.
Galluccio, L., Michel, O.J.J., Comon, P., Hero III, A.O.: Graph based k-means clustering. Sig. Process. 92(9), 1970–1984 (2012)CrossRef
26.
27.
Bunke, H., Riesen, K.: Improving vector space embedding of graphs through feature selection algorithms. Pattern Recogn. 44(9), 1928–1940 (2011)CrossRef
28.
Liu, L., Wang, L., Liu, X.: In defense of soft-assignment coding. In: 2011 IEEE International Conference on IEEE Computer Vision (ICCV), pp. 2486–2493 (2011)
29.
Campbell, C., Ying, Y.: Learning with support vector machines. Synth. Lect. Artif. Intell. Mach. Learn. 5(1), 1–95 (2011)CrossRef
30.
Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 3, pp. 32–36. IEEE (2004)
31.
Serratosa, F., Cortés, X.: Graph edit distance: moving from global to local structure to solve the graph-matching problem. Pattern Recogn. Lett. 65, 204–210 (2015)CrossRef
32.
33.
Bregonzio, M., Xiang, T., Gong, S.: Fusing appearance and distribution information of interest points for action recognition. Pattern Recogn. 45(3), 1220–1234 (2012)CrossRef
34.
Klaser, A., Marszałek, M., Schmid, C.: A spatio-temporal descriptor based on 3D-gradients. In: BMVC 2008-19th British Machine Vision Conference, pp. 275:1–10. British Machine Vision Association (2008)
35.
36.
Dollár, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal features. In: 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 65–72. IEEE (2005)
Metadata
Title
Bags of Graphs for Human Action Recognition
Authors
Xavier Cortés
Donatello Conte
Hubert Cardot
Copyright Year
2018
Book
Structural, Syntactic, and Statistical Pattern Recognition

Print ISBN: 978-3-319-97784-3

Electronic ISBN: 978-3-319-97785-0

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-97785-0_41

Premium Partner

    Image Credits