Title: Enhancing the retrieval performance in content based image retrieval using meta-heuristic approach
Authors: S. Umamaheswaran; N. Suresh Kumar; K. Ganesh; P. Sivakumar
Addresses: Department of Computer Science and Engineering, Vickram College of Engineering, Enathi, Sivagangai, Madurai – 630561, India ' Velammal College of Engineering and Technology, Velammal Nagar, Viraganoor, Madurai – 625009, Tamilnadu, India ' Supply Chain Management – Center of Competence, McKinsey Knowledge Center India Private Limited, McKinsey & Company, Chennai, India ' Department of Mechanical Engineering, Mahsa University, Malaysia
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.
International Journal of Business Information Systems, 2017 Vol.26 No.2, pp.220 - 235
Received: 10 Mar 2016
Accepted: 06 May 2016
Published online: 15 Aug 2017 *