DocumentCode :
2917062
Title :
Semantic rules classification for images annotation
Author :
Ayadi, Yassine ; Amous, Ikram ; Gargouri, Mohamed ; Gargouri, Faiez
Author_Institution :
MIRACL, Sfax, Tunisia
fYear :
2011
fDate :
5-8 Dec. 2011
Firstpage :
72
Lastpage :
77
Abstract :
In this paper, we present an approach to facilitate the annotation and the retrieval of the image documents. Our approach is based on the definition and the generation of semantic rules presented via the logic of predicates. Then, we proposed the classification of these rules by a method of clustering Fuzzy C-means. For this, we used the method of co-citation to calculate the similarity measure between images. This classification has grouped thematically these images with the aim to facilitate research and annotation. To validate our proposal, we implemented a tool tested on a set of images of the city center. Finally, we conducted a series of tests to evaluate our approach.
Keywords :
document image processing; fuzzy set theory; image classification; image retrieval; pattern clustering; fuzzy C mean clustering; image document retrieval; images annotation; semantic rules classification; Cities and towns; Classification algorithms; Databases; Hidden Markov models; Hybrid intelligent systems; Ontologies; Semantics; Annotation; Classification; Fuzzy C-means; Image; Ontology; Semantics Rules;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Hybrid Intelligent Systems (HIS), 2011 11th International Conference on
Conference_Location :
Melacca
Print_ISBN :
978-1-4577-2151-9
Type :
conf
DOI :
10.1109/HIS.2011.6122083
Filename :
6122083
Link To Document :
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