Integration of Context Information through Probabilistic Ontological Knowledge into Image Classification
| Autor: | Anna Corazza, Giuseppe Vettigli, Andrea Apicella, Francesco Isgrò |
|---|---|
| Přispěvatelé: | Apicella, Andrea, Corazza, Anna, Isgrò, Francesco, Vettigli, Giuseppe |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Computer science
02 engineering and technology Machine learning computer.software_genre 050105 experimental psychology probabilistic model Ontological knowledge 0202 electrical engineering electronic engineering information engineering Contextual information 0501 psychology and cognitive sciences probabilistic ontology Contextual image classification lcsh:T58.5-58.64 business.industry lcsh:Information technology Probabilistic ontology 05 social sciences Probabilistic logic Statistical model A priori and a posteriori 020201 artificial intelligence & image processing Artificial intelligence business computer Classifier (UML) image object recognition Information Systems |
| Zdroj: | Information Volume 9 Issue 10 Information, Vol 9, Iss 10, p 252 (2018) |
| ISSN: | 2078-2489 |
| DOI: | 10.3390/info9100252 |
| Popis: | 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&rsquo 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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