Applying Fuzzy DLs in the Extraction of Image Semantics
By Stamatia Dasiopoulou, Yiannis Kompatsiaris, Michael Gerasimos Strintzis. 2009
29 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Luciano Serafini
- A.S. d'Avila Garcez
- Ivan Donadello
- Mas Rina Mustaffa
- Shyamala C. Doraisamy
- Alfian Abdul Halin
- Mohsen Zand
- L. C. Lamb
In Computer Vision and Pattern Recognition
- A.S. d'Avila Garcez
- Luciano Serafini
- Ivan Donadello
- Mas Rina Mustaffa
- Shyamala C. Doraisamy
- Alfian Abdul Halin
- Mohsen Zand
- Gesina Schwalbe
Citations in the corpus
- Logic Tensor Networks for Semantic Image Interpretation (2017, methodology): “Then, with logical reasoning, new facts can be derived in the scene from these basic components [19, 21]. Other logic-based approaches have used fuzzy DL to tackle uncertainty in the basic components [14, 6, 1]. These approaches have limited themselves to spatial relations or to refining the labels o”
- Fuzzy Ontology Representation using OWL 2 (2010, unknown): “Key words: Fuzzy OWL 2, Fuzzy Ontologies, Fuzzy Languages for the Semantic Web, Fuzzy Description Logics”
- Integration of numeric and symbolic information for semantic image interpretation (2016, unknown): “[7]usedfuzzylogictoresolvesthe inconsistencyofthelabelsextractedfromlow-level imageanalysis.”
- Ontology-Based Semantic Image Segmentation Using Mixture Models and Multiple CRFs (2016, methodology): “In [36], an ontology-based framework was proposed to”