ICSI 2016: Advances in Swarm Intelligence pp 392-400 | Cite as

Content-Based Image Retrieval Based on Quantum-Behaved Particle Swarm Optimization Algorithm

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9712)

Abstract

The performance of content-based image retrieval (CBIR) is usually limited since only single visual feature and single similarity measurement are used. In order to solve this problem, the color and texture visual features of an image are analyzed firstly. And then 12 kinds of similarity measurement are used to evaluate similarity between the image being checked and the images in the retrieval library. The CBIR problem is therefore transferred to an optimization problem with the precision ratio as its objective function. Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is used to solve the CBIR optimization problem in order to find the optimal weight and the optimal combination of visual features and similarity measurements. Experimental results show that the proposed method based on QPSO algorithm has better performance on the retrieval effect.

Keywords

Content based image retrieval Quantum-behaved Particle Swarm Optimization Feature extraction 
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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.School of Internet of Things EngineeringJiangnan UniversityWuxiChina

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