Title :
Algorithms for reducing the semantic gap in image retrieval systems
Author :
Ion, Anca Loredana
Author_Institution :
Univ. of Craiova, Craiova
Abstract :
In this paper we study the possibilities to discover correlations between visual primitive characteristics and semantic concepts of images, meaning the extraction of semantic meaning based on learning, from an image database. The problem of automatic discovery of semantic inference rules is approached. A semantic rule is a combination of semantic indicator values, which are visual elements, that identifies semantic concepts of images. The annotation procedure starts with the semantic rules generation on each image category. The language used for rules representation is Prolog. The advantages of using Prolog are its flexibility and simplicity in representation of rules. Our methods are not limited to any specific domain and they can be applied in any field.
Keywords :
PROLOG; data mining; feature extraction; image retrieval; inference mechanisms; visual databases; Prolog; association rules; image annotation; image database; image retrieval systems; semantic gap algorithms; semantic inference rules; Association rules; Data mining; Humans; Image databases; Image retrieval; Image segmentation; Shape; Spatial coherence; Spatial databases; Visual databases; association rules; image annotation; mining images; visual features;
Conference_Titel :
Human System Interactions, 2009. HSI '09. 2nd Conference on
Conference_Location :
Catania
Print_ISBN :
978-1-4244-3959-1
Electronic_ISBN :
978-1-4244-3960-7
DOI :
10.1109/HSI.2009.5090961
