Algorithms for reducing the semantic gap in image retrieval systems

Author

Ion, Anca Loredana

Author_Institution

Univ. of Craiova, Craiova

fYear

2009

fDate

21-23 May 2009

Firstpage

97

Lastpage

102

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/HSI.2009.5090961

Filename

5090961