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首页> 外文期刊>Arabian Journal for Science and Engineering >Content-Based Image Retrieval Using Color, Shape and Texture Descriptors and Features
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Content-Based Image Retrieval Using Color, Shape and Texture Descriptors and Features

机译：使用颜色，形状和纹理描述符和特征的基于内容的图像检索

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Due to the recent technology development, the multimedia complexity is noticeably increased and new research areas areopened relying on similarmultimedia content retrieval. Content-based image retrieval (CBIR) systems are used for the retrievalof images related to the Query Image (QI) from huge databases. The CBIR systems available today have confined efficiencyas they extract only limited feature sets. This paper demonstrates the extraction of vast robust and important features fromthe images database and the storage of these features in the repository in the form of feature vectors. The feature repositorycontains color signature, the shape features and texture features. Here, features are extracted from specific QI. Accordingly,an innovative similarity evaluation with a metaheuristic algorithm (genetic algorithm with simulating annealing) has beenattained between the QI features and those belonging to the database images. For an image entered as QI from a database, thedistance metrics are used to search the related images, which is the main idea of CBIR. The proposed CBIR techniques aredescribed and constructed based onRGBcolor with neutrosophic clustering algorithm and Canny edge method to extract shapefeatures, YCbCr color with discrete wavelet transform and Canny edge histogram to extract color features, and gray-levelco-occurrence matrix to extract texture features. The combination of these methods increases the image retrieval frameworkperformance for content-based retrieval. Furthermore, the results’ precision–recall value is calculated to evaluate the system’sefficiency. The CBIR system proposed demonstrates better precision and recall values compared to other state-of-the-artCBIR systems.
机译：由于最近的技术发展，多媒体的复杂性显着增加，并且依靠类似的多媒体内容检索打开了新的研究领域。基于内容的图像检索（CBIR）系统用于从大型数据库中检索与查询图像（QI）相关的图像。如今可用的CBIR系统仅提取有限的功能集，因此效率有限。本文演示了从图像数据库中提取大量强大而重要的特征，并以特征向量的形式将这些特征存储在存储库中。特征库包含颜色签名，形状特征和纹理特征。在此，从特定QI中提取特征。因此，在QI特征和那些属于数据库图像的特征之间已经获得了利用元启发式算法（具有模拟退火的遗传算法）的创新相似性评估。对于从数据库输入为QI的图像，距离度量用于搜索相关图像，这是CBIR的主要思想。基于中性聚类算法的RGBcolor和Canny边缘方法提取形状特征，使用离散小波变换使用YCbCr颜色和Canny边缘直方图提取颜色特征，并使用灰度级共生矩阵提取纹理特征，基于RGBcolor描述和构造了所提出的CBIR技术。这些方法的组合提高了基于内容的检索的图像检索框架性能。此外，计算结果的查准率值可以评估系统的效率。与其他最新的CBIR系统相比，建议的CBIR系统具有更好的精度和召回率。

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