Title :
Image classification and retrieval based on crisp and Fuzzy ontology
Author_Institution :
Software Eng. Dept., Univ. of Eng. & Technol., Taxila, Pakistan
Abstract :
This paper proposes crisp and fuzzy ontology model for reducing the Semantic gap between the user requirements and the System model in order to provide better image classification and retrieval. The approach is based on building ontology of natural scenes using Protégé for querying in more natural way and then integrating fuzzy logic to improve the image retrieval.
Keywords :
content-based retrieval; fuzzy logic; fuzzy set theory; image classification; image retrieval; natural scenes; ontologies (artificial intelligence); Protege; crisp ontology; fuzzy logic; fuzzy ontology; image classification; image querying; image retrieval; natural scene; ontology building; semantic gap reduction; system model; user requirement; Feature extraction; Fuzzy logic; Image classification; Image retrieval; Ontologies; Semantics; Vegetation; Fuzzy Ontology; Ontology; Semantic Web;
Conference_Titel :
Computer,Control & Communication (IC4), 2013 3rd International Conference on
Conference_Location :
Karachi
Print_ISBN :
978-1-4673-6011-1
DOI :
10.1109/IC4.2013.6653737
