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Using visual features to design a content-based image retrieval method optimized by particle swarm optimization algorithm

机译：利用视觉特征设计粒子群优化算法优化的基于内容的图像检索方法

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摘要

This paper presents a content-based image retrieval method using three kinds of visual features and 12 distance measurements, which is optimized by particle swarm optimization (PSO) algorithm. For convenience, it is called the CBIRVP method hereafter. First, the CBIRVP method extracts three kinds of features: color, texture, and shape features of images. Subsequently, it employs appropriate distance measurements for each kind of features to calculate the similarities between a query image and others in the database D. Also, the PSO algorithm is utilized to optimize the CBIRVP method via searching for nearly optimal combinations between the features and their corresponding similarity measurements, as well as finding out the approximately optimal weights for three similarities with respect to three kinds of features. Finally, experimental results demonstrate that the CBIRVP method outperforms other existing methods under consideration here.
机译：本文提出了一种基于内容的图像检索方法，该方法利用三种视觉特征和12个距离测量值，并通过粒子群优化（PSO）算法对其进行了优化。为了方便起见，以下将其称为CBIRVP方法。首先，CBIRVP方法提取三种特征：图像的颜色，纹理和形状特征。随后，它对每种特征采用适当的距离测量，以计算查询图像与数据库D中其他特征之间的相似度。此外，PSO算法还通过搜索特征及其特征之间的最佳组合来优化CBIRVP方法。相应的相似性度量，以及针对三种特征找出三种相似性的近似最佳权重。最后，实验结果表明，CBIRVP方法优于此处考虑的其他现有方法。

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