The main problem in the Content based image retrieval is “semantic gap”. A human match two images related to its semantics, while a retrieval system do the same process based on comparison of feature vectors corresponding to visual image features. The problem in retieval of images is identifying its unique features. This article proposes three different methods to retrieve images based on the unique features of an image and also evaluates the retrieval system performance. The first approach called Surrounding information retrieval (SIR) extracts the features related to the similarities of the neighborhood intensity values. The second approach Minimum edge retrieval (MER) identifies the minimum intensity value of each block. The third approach integrated feature retrieval (IFR) combines the properties of feature extraction from SIR and MER. The extracted unique features are stored in a feature dataset and the similar images are retrieved by comparing the dataset using distance measure. The performance of retrieval system is calculated in terms of its recall and precision. The precision and recall values are superior than the existing methods. The method IFR with multi feature extraction shows good in retrieval accuracy compared with other methods.

基于内容的图像检索中的主要问题是“语义间隙”。一个人匹配与其语义相关的两幅图像，而一个检索系统则基于比较与视觉图像特征相对应的特征向量来执行相同的过程。图像检索中的问题是确定其独特功能。本文提出了三种不同的方法来基于图像的独特特征来检索图像，并评估了检索系统的性能。第一种方法称为周围信息检索（SIR），它提取与邻域强度值相似度有关的特征。第二种方法最小边缘检索（MER）标识每个块的最小强度值。第三种方法集成特征检索（IFR）结合了从SIR和MER中提取特征的特性。所提取的唯一特征存储在特征数据集中，并且通过使用距离量度比较数据集来检索相似图像。检索系统的性能是根据其查全率和准确性来计算的。精度和查全率值优于现有方法。与其他方法相比，具有多特征提取的IFR方法显示出良好的检索精度。