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Information 2018, 9(10), 252; https://doi.org/10.3390/info9100252

Integration of Context Information through Probabilistic Ontological Knowledge into Image Classification

Dipartimento di Ingegneria Elettrica e delle Tecnologie dell’Informazione, Università degli Studi di Napoli Federico II, Via Claudio 21, 80125 Napoli, Italy
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Received: 26 August 2018 / Revised: 21 September 2018 / Accepted: 9 October 2018 / Published: 12 October 2018
(This article belongs to the Special Issue Advanced Learning Methods for Complex Data)
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Abstract

The use of ontological knowledge to improve classification results is a promising line of research. The availability of a probabilistic ontology raises the possibility of combining the probabilities coming from the ontology with the ones produced by a multi-class classifier that detects particular objects in an image. This combination not only provides the relations existing between the different segments, but can also improve the classification accuracy. In fact, it is known that the contextual information can often give information that suggests the correct class. This paper proposes a possible model that implements this integration, and the experimental assessment shows the effectiveness of the integration, especially when the classifier’s accuracy is relatively low. To assess the performance of the proposed model, we designed and implemented a simulated classifier that allows a priori decisions of its performance with sufficient precision. View Full-Text
Keywords: image object recognition; probabilistic ontology; probabilistic model image object recognition; probabilistic ontology; probabilistic model
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Apicella, A.; Corazza, A.; Isgrò, F.; Vettigli, G. Integration of Context Information through Probabilistic Ontological Knowledge into Image Classification. Information 2018, 9, 252.

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