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Exploiting Contextual Knowledge for Hybrid Classification of Visual Objects

Part of the Lecture Notes in Computer Science book series (LNAI,volume 10021)

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.

Keywords

  • Scale Invariant Feature Transform
  • Semantic Context
  • Local Classifier
  • Weak Constraint
  • Context Constraint

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This research has been supported by the Austrian Science Fund (FWF) projects P27730 and W1255-N23.

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Notes

  1. 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.

  2. 2.

    http://opencv.org/.

  3. 3.

    https://pypi.python.org/pypi/Shapely.

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Correspondence to Tobias Kaminski .

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Eiter, T., Kaminski, T. (2016). Exploiting Contextual Knowledge for Hybrid Classification of Visual Objects. In: Michael, L., Kakas, A. (eds) Logics in Artificial Intelligence. JELIA 2016. Lecture Notes in Computer Science(), vol 10021. Springer, Cham. https://doi.org/10.1007/978-3-319-48758-8_15

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

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