Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Metabolic Pathways as Temporal Logic Programs Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Exploiting Contextual Knowledge for Hybrid Classification of Visual Objects

verfasst von : Thomas Eiter, Tobias Kaminski

Erschienen in: Logics in Artificial Intelligence

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

We consider the problem of classifying visual objects in a scene by exploiting the semantic context. For this task, we define hybrid classifiers (HC) that combine local classifiers with context constraints, and can be applied to collective classification problems (CCPs) in general. Context constraints are represented by weighted ASP constraints using object relations. To integrate probabilistic information provided by the classifier and the context, we embed our encoding in the formalism \(LP^{MLN}\), and show that an optimal labeling can be efficiently obtained from the corresponding \(LP^{MLN}\) program by employing an ordinary ASP solver. Moreover, we describe a methodology for constructing an HC for a CCP, and present experimental results of applying an HC for object classification in indoor and outdoor scenes, which exhibit significant improvements in terms of accuracy compared to using only a local classifier.

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 Prompt Interval Temporal Logic
Nächstes Kapitel Reasoning About Justified Belief Based on the Fusion of Evidence
Fußnoten
1
As the logic program rules here are more restricted than in [10], we adapt the translation defined there. Real-valued weights can be approximated by integers in weak constraints.
 
Literatur
1.
Angin, P., Bhargava, B.: A confidence ranked co-occurrence approach for accurate object recognition in highly complex scenes. J. Internet Technol. 14(1), 13–19 (2013)
2.
Buccafurri, F., Leone, N., Rullo, P.: Enhancing disjunctive datalog by constraints. IEEE Trans. Knowl. Data Eng. 12(5), 845–860 (2000)CrossRef
3.
Chechetka, A., Dash, D., Philipose, M.: Relational learning for collective classification of entities in images. In: AAAI Workshops on Statistical Relational Artificial Intelligence, AAAI Workshop 2010. AAAI (2010)
4.
Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV 2004, pp. 1–22 (2004)
5.
Eiter, T., Ianni, G., Schindlauer, R., Tompits, H.: A uniform integration of higher-order reasoning and external evaluations in answer-set programming. In: International Joint Conference on Artificial Intelligence, IJCAI 2005, pp. 90–96. Professional Book Center (2005)
6.
Galleguillos, C., Rabinovich, A., Belongie, S.J.: Object categorization using co-occurrence, location and appearance. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2008. IEEE Computer Society (2008)
7.
Gebser, M., Kaufmann, B., Kaminski, R., Ostrowski, M., Schaub, T., Schneider, M.T.: Potassco: the potsdam answer set solving collection. AI Commun. 24(2), 107–124 (2011)MathSciNetMATH
8.
Gelfond, M., Lifschitz, V.: Classical negation in logic programs and disjunctive databases. New Gener. Comput. 9(3/4), 365–386 (1991)CrossRefMATH
9.
Getoor, L.: Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)MATH
10.
Lee, J., Wang, Y.: Weighted rules under the stable model semantics. In: Proceedings of the Fifteenth International Conference on Principles of Knowledge Representation and Reasoning, KR 2016, pp. 145–154. AAAI Press (2016)
11.
London, B., Getoor, L.: Collective classification of network data. In: Aggarwal, C.C. (ed.) Data Classification: Algorithms and Applications, pp. 399–416. CRC Press, Boca Raton (2014)
12.
Lowe, D.G.: Object recognition from local scale-invariant features. In: 7th IEEE International Conference on Computer Vision, ICCV 1999, pp. 1150–1157 (1999)
13.
Marton, Z.C., Rusu, R.B., Jain, D., Klank, U., Beetz, M.: Probabilistic categorization of kitchen objects in table settings with a composite sensor. In: International Conference on Intelligent Robots and Systems, IEEE/RSJ 2009, pp. 4777–4784. IEEE (2009)
14.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., VanderPlas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetMATH
15.
Rabinovich, A., Belongie, S.J.: Scenes vs. objects: a comparative study of two approaches to context based recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR Workshops 2009, pp. 92–99. IEEE Computer Society (2009)
16.
Rabinovich, A., Vedaldi, A., Galleguillos, C., Wiewiora, E., Belongie, S.J.: Objects in context. In: IEEE 11th International Conference on Computer Vision, ICCV 2007, pp. 1–8. IEEE Computer Society (2007)
17.
Randell, D.A., Cui, Z., Cohn, A.G.: A spatial logic based on regions and connection. In: Proceedings of the 3rd International Conference on Principles of Knowledge Representation and Reasoning, KR 1992, pp. 165–176. Morgan Kaufmann (1992)
18.
Richardson, M., Domingos, P.M.: Markov logic networks. Mach. Learn. 62(1–2), 107–136 (2006)CrossRef
19.
Russell, B.C., Torralba, A., Murphy, K.P., Freeman, W.T.: LabelMe: a database and web-based tool for image annotation. Int. J. Comput. Vis. 77(1–3), 157–173 (2008)CrossRef
20.
Saathoff, C., Staab, S.: Exploiting spatial context in image region labelling using fuzzy constraint reasoning. In: Ninth International Workshop on Image Analysis for Multimedia Interactive Services, WIAMIS 2008, pp. 16–19. IEEE Computer Society (2008)
21.
Sen, P., Namata, G., Bilgic, M., Getoor, L.: Collective classification. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 189–193. Springer, US (2010)
22.
Sen, P., Namata, G., Bilgic, M., Getoor, L., Gallagher, B., Eliassi-Rad, T.: Collective classification in network data. AI Mag. 29(3), 93–106 (2008)
23.
Strobl, C.: Dimensionally extended nine-intersection model (DE-9IM). In: Shekhar, S., Xiong, H. (eds.) Encyclopedia of GIS, pp. 240–245. Springer, US (2008)CrossRef
24.
Tran, S.D., Davis, L.S.: Event modeling and recognition using Markov logic networks. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5303, pp. 610–623. Springer, Heidelberg (2008). doi:10.​1007/​978-3-540-88688-4_​45 CrossRef
25.
Zhang, H., Fritts, J.E., Goldman, S.A.: Image segmentation evaluation: a survey of unsupervised methods. Comput. Vis. Image Underst. 110(2), 260–280 (2008)CrossRef
Metadaten
Titel
Exploiting Contextual Knowledge for Hybrid Classification of Visual Objects
verfasst von
Thomas Eiter
Tobias Kaminski
Copyright-Jahr
2016
Buch
Logics in Artificial Intelligence

Print ISBN: 978-3-319-48757-1

Electronic ISBN: 978-3-319-48758-8

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-48758-8_15

Premium Partner

    Bildnachweise