nach oben

Erschienen in:

2018 | OriginalPaper | Buchkapitel

Selective and Simple Graph Structures for Better Description of Local Point-Based Image Features

verfasst von : Grzegorz Kurzejamski, Marcin Iwanowski

Erschienen in: Computer Vision and Graphics

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The paper presents simple graph features based on a well-known image keypoints. We discuss the extraction method and geometrical properties that can be used. Chosen methods are tested in KNN tasks for almost 1000 object classes. The approach addresses problems in applications that cannot use learning methods explicitly, as real-time tracking, chosen object detection scenarios and structure from motion. Results imply that the idea is worth further research for chosen systems.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren

Vorheriges Kapitel A System for Automatic Town Sign Recognition for Driver Assistance Systems

Nächstes Kapitel Scene Recognition for Indoor Localization of Mobile Robots Using Deep CNN

Alahi, A., Ortiz, R., Vandergheynst, P.: FREAK: fast retina keypoint. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2012, pp. 510–517, June 2012

Azad, P., Asfour, T., Dillmann, R.: Combining harris interest points and the SIFT descriptor for fast scale-invariant object recognition. In: IEEE/RSJ International Conference on Intelligent Robots and Systems 2009. IROS 2009, pp. 4275–4280, October 2009

Bai, L., Hancock, E.R.: Fast depth-based subgraph kernels for unattributed graphs. Pattern Recogn. 50, 233–245 (2016). https://doi.org/10.1016/j.patcog.2015.08.006CrossRef

Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008). Similarity Matching in Computer Vision and MultimediaCrossRef

Caetano, T.S., McAuley, J.J., Cheng, L., Le, Q.V., Smola, A.J.: Learning graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 31(6), 1048–1058 (2009). https://doi.org/10.1109/Ftpami.2009.28

Cantoni, V., Cinque, L., Guerra, C., Levialdi, S., Lombardi, L.: 2-D object recognition by multiscale tree matching. Pattern Recognit. 31(10), 1443–1454 (1998)CrossRef

Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2005, CVPR 2005, vol. 1, pp. 886–893, June 2005

Dean, T., Ruzon, M., Segal, M., Shlens, J., Vijayanarasimhan, S., Yagnik, J.: Fast, accurate detection of 100,000 object classes on a single machine. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, USA (2013)

Demirci, M.F., Shokoufandeh, A., Keselman, Y., Bretzner, L., Dickinson, S.: Object recognition as many-to-many feature matching. Int. J. Comput. Vis. 69(2), 203–222 (2006). https://doi.org/10.1007/Fs11263-006-6993-y

10.

Duchenne, O., Bach, F., Kweon, I.S., Ponce, J.: A tensor-based algorithm for high-order graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2383–2395 (2011). https://doi.org/10.1109/Ftpami.2011.110

11.

Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)CrossRef

12.

Geusebroek, J.M., Burghouts, G.J., Smeulders, A.W.: The amsterdam library of object images, January 2005. https://doi.org/10.1023/B:VISI.0000042993.50813.60CrossRef

13.

Han, X., Leung, T., Jia, Y., Sukthankar, R., Berg, A.C.: Matchnet: unifying feature and metric learning for patch-based matching. In: CVPR (2015)

14.

Kurzejamski, G., Zawistowski, J., Sarwas, G.: A framework for robust object multi-detection with a vote aggregation and a cascade filtering. CoRR abs/1512.08648 (2015). http://arxiv.org/abs/1512.08648

15.

Kurzejamski, G., Zawistowski, J., Sarwas, G.: Robust method of vote aggregation and proposition verification for invariant local features. In: VISAPP 2015 - Proceedings of the International Conference on Computer Vision Theory and Applications, pp. 593–600, March 2015

16.

Lee, J., Cho, M., Lee, K.M.: Hyper-graph matching via reweighted random walks. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1633–1640. IEEE, June 2011. https://doi.org/10.1109/Fcvpr.2011.5995387

17.

Leutenegger, S., Chli, M., Siegwart, R.: BRISK: binary robust invariant scalable keypoints. In: IEEE International Conference on Computer Vision (ICCV) 2011, pp. 2548–2555, November 2011

18.

Loquercio, A., Dymczyk, M., Zeisl, B., Lynen, S., Gilitschenski, I., Siegwart, R.: Efficient descriptor learning for large scale localization. In: ICRA (2017)

19.

Lowe, D.: Distinctive Image Features From Scale-invariant Keypoints, vol. 60, pp. 91–110. Kluwer Academic Publishers, Hingham (2004)MathSciNetCrossRef

20.

Lowe, D.: Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision 1999, vol. 2, pp. 1150–1157 (1999)

21.

Lv, X., Wang, Z.J.: Perceptual image hashing based on shape contexts and local feature points. IEEE Trans. Inf. Forensics Secur. 7(3), 1081–1093 (2012). https://doi.org/10.1109/Ftifs.2012.2190594

22.

Myeong, H., Chang, J.Y., Lee, K.M.: Learning object relationships via graph-based context model. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2727–2734. IEEE, June 2012. https://doi.org/10.1109/Fcvpr.2012.6247995

23.

Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: An efficient alternative to SIFT or SURF. In: IEEE International Conference on Computer Vision (ICCV) 2011, pp. 2564–2571, November 2011

24.

Shervashidze, N., Schweitzer, P., van Leeuwen, E.J., Mehlhorn, K., Borgwardt, K.M.: Weisfeiler-lehman graph kernels. J. Mach. Learn. Res. 12, 2539–2561 (2011). http://dl.acm.org/citation.cfm?id=1953048.2078187

25.

Simo-Serra, E., Trulls, E., Ferraz, L., Kokkinos, I., Fua, P., Moreno-Noguer, F.: Discriminative learning of deep convolutional feature point descriptors. In: ICCV (2015)

26.

Tian, Y., Patel, J.M.: Tale: a tool for approximate large graph matching. In: 2008 IEEE 24th International Conference on Data Engineering (ICDE 2008), pp. 963–972. IEEE, April 2008. https://doi.org/10.1109/Ficde.2008.4497505

27.

Torresani, L., Kolmogorov, V., Rother, C.: Feature correspondence via graph matching: models and global optimization. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5303, pp. 596–609. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88688-4_44CrossRef

28.

Vento, M.: A long trip in the charming world of graphs for pattern recognition. Pattern Recogn. 48(2), 291–301, February 2015. https://doi.org/10.1016/Fj.patcog.2014.01.002

29.

Yi, K.M., Trulls, E., Lepetit, V., Fua, P.: Lift: Learned invariant feature transform. In: ECCV (2016)

30.

Zass, R., Shashua, A.: Probabilistic graph and hypergraph matching. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8. IEEE, June 2008. https://doi.org/10.1109/Fcvpr.2008.4587500

31.

Zickler, S., Efros, A.: Detection of multiple deformable objects using PCA-SIFT. In: Proceedings of the 22nd National Conference on Artificial Intelligence, AAAI 2007, vol. 2, pp. 1127–1132. AAAI Press (2007)

Titel: Selective and Simple Graph Structures for Better Description of Local Point-Based Image Features
verfasst von: Grzegorz Kurzejamski
Marcin Iwanowski
Verlag: Springer International Publishing
Buch: Computer Vision and Graphics
Print ISBN: 978-3-030-00691-4

Electronic ISBN: 978-3-030-00692-1

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-00692-1_12

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"