PSO Optimized Log Gabor QBIC System
N. Jyothi1, D. Madhavi2
1N.Jyothi, Department of EIE,GITAM Deemed to be University, Visakhapatnam, Andhra Pradesh,India.
2D. Madhavi, Department of ECE,GITAM Deemed to be University, Visakhapatnam, Andhra Pradesh, India.
Manuscript received on 22 August 2019. | Revised Manuscript received on 05 September 2019. | Manuscript published on 30 September 2019. | PP: 1650-1654 | Volume-8 Issue-11, September 2019. | Retrieval Number: K19140981119/2019©BEIESP | DOI: 10.35940/ijitee.K1914.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In modern years, there is substantially technical progression in research area pertaining to image retrieval, in specific Query By Image Content (QBIC) system. It has turned out to be essential to deliver adept and effective method to retrieve images from the gigantic collections of images utilized in heterogeneous applications. In this paper, a hybrid QBIC retrieval system known to be PSO optimized Log Gabor QBIC system that retrieves color features, texture features and shape features of the images in three consecutive stages has been developed. In the proposed system, color features are retrieved by means of color histogram in the first stage. In subsequent stage, the texture features are extracted by tuning Log Gabor filters using Particle Swarm Optimization(PSO). Lastly, shape features are retrieved by polygonal fitting algorithm. The recommended method displays enhanced retrieval rate in terms of mean recall and mean precision when compared to the prevailing standard systems.
Keywords: Particle Swarm Optimization, mean recall and mean precision.
Scope of the Article: Swarm Intelligence