Title: An effective similarity measure via genetic algorithm for Content-Based Image Retrieval with extensive features
Authors: Baddeti Syam; Yerravarapu Srinivasa Rao
Addresses: Mandava Institute of Engineering and Technology, Jaggayyapet 521 275, Andhra Pradesh, India ' Instrument Technology Department, AU College of Engineering, Andhra University, Visakhapatnam, Andhra Pradesh, India
Abstract: With the aid of image content, the relevant images can be extracted from the image in the Content Based Image Retrieval (CBIR) system. Concise feature sets limit the retrieval efficiency, to eliminate this shape, colour, texture and contourlet features are extracted. For retrieving relevant images, the optimisation technique Genetic Algorithm (GA) is utilised and for similarity measure Squared Euclidean Distance (SED) is utilised for comparing query image featureset and database image featureset. Hence, from GA based similarity measure, relevant images are retrieved and evaluated by querying different images.
Keywords: CBIR; content based image retrieval; GAs; genetic algorithms; SED; squared euclidean distance; extensive features; imaging systems; shape features; similarity measures.
International Journal of Signal and Imaging Systems Engineering, 2012 Vol.5 No.1, pp.18 - 28
Available online: 04 May 2012 *Full-text access for editors Access for subscribers Purchase this article Comment on this article