| Home  | About ScienceAsia  | Publication charge  | Advertise with us  | Subscription for printed version  | Contact us  
Editorial Board
Journal Policy
Instructions for Authors
Online submission
Author Login
Reviewer Login
Volume 43 Number 4
Volume 43 Number 3
Volume 43 Number 2
Volume 43 Number 1
Volume 43S Number 1
Volume 42 Number 6
Earlier issues
Volume 42 Number 5 Volume 42S Number 1

previous article next article

Research articles

ScienceAsia 42S(2016): 34-41 |doi: 10.2306/scienceasia1513-1874.2016.42S.034


An evolutionary approach for optimizing content-based image retrieval using a support vector machine


T.˙Kanimozhi*, K.˙Latha

 
ABSTRACT:     One of the vital challenges in the field of image retrieval is the semantic gap between visual features and high-level semantic concepts. Various kinds of relevance feedback approaches have been developed to deal with this semantic gap. Of these, the support vector machine is important. The support vector machine relies upon its parameters and the number of feedback samples. This paper uses a Gaussian firefly algorithm along with support vector machine to raise the relevance feedback performance. The proposed approach determines the parameters of the support vector machine and increases the number of relevant feedback samples, thereby optimizing the retrieval process. The performance of the proposed approach is compared with other existing retrieval methods.

Download PDF

8 Download 72 View


Department˙of˙Computer˙Science˙and˙Engineering, University˙College˙of˙Engineering, BIT˙Campus, Tiruchirappalli˙620024, Tamilnadu, India

* Corresponding author, E-mail: csrkani@gmail.com

Received 31 Aug 2014, Accepted 20 Jul 2016