Enhancing the retrieval performance in content based image retrieval using meta-heuristic approach

S. Umamaheswaran, N. Suresh Kumar, K. Ganesh and P. Sivakumar

International Journal of Business Information Systems, 2017, vol. 26, issue 2, 220-235

Abstract: Multimedia and digital image database management have undergone major transformation significantly in recent years in domains like data mining, medical imaging, weather forecasting and remote sensing, etc. CBIR is an important tool, useful to retrieve the required image precisely and effectively from large databases. This paper proposes the use of low level visual content features viz, mean value and colour histogram, to retrieve the colour feature and gray level co-occurrence matrix is proposed for retrieval of the texture feature. IGA is integrated with the above said low level features to derive refined results for query image matching from the database and to differentiate retrieval performance among the image features. The proposed method yields an accurate and faster retrieval from a Corel image database.

Keywords: interactive genetic algorithm; IGA; content-based image retrieval; CBIR; gray level cooccurrence matrix; GLCM; colour histogram; mean. (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.inderscience.com/link.php?id=86333 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbisy:v:26:y:2017:i:2:p:220-235

Access Statistics for this article

More articles in International Journal of Business Information Systems from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().