The current approaches for image retrieval are more concentrating on numerous image features. Texture, shape, spatial information, and color are the fundamental features to deal with flexible image datasets. This paper aims to develop new Content‐Based Image Retrieval System based on Optimal Weighted Hybrid Pattern. Two relevant patters like Local Vector Pattern and Local Derivative Pattern are intended to develop a novel Content‐Based Image Retrieval system. The optimal weighted hybrid pattern is implemented to derive a new feature vector, so that the weight is optimized by a modified optimization algorithm called Improved Local Leader‐based Spider Monkey Optimization to maximize the precision and recall of the retrieved images. The retrieval of the image is done by measuring the similarity based on Mean Square Distance between the features of query image as well as training image. Finally, the performance comparison of the proposed and the traditional patterns shows its reliable performance.

中文翻译：

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基于改进的蜘蛛猴优化的基于内容的医学图像检索最优加权混合模式

当前的图像检索方法更多地集中在众多图像特征上。纹理，形状，空间信息和颜色是处理灵活的图像数据集的基本特征。本文旨在基于最佳加权混合模式开发新的基于内容的图像检索系统。有两个相关的模式，例如“局部矢量模式”和“局部导数模式”，旨在开发一种新颖的基于内容的图像检索系统。实现最佳加权混合模式以导出新的特征向量，从而通过一种名为“基于改进的本地领导者的蜘蛛猴优化”的改进优化算法来优化权重，以最大程度地提高检索图像的精度和召回率。通过基于查询图像和训练图像的特征之间的均方距离测量相似度来完成图像的检索。最后，将本文提出的模式与传统模式进行性能比较，结果表明了其可靠的性能。