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Experimental analogy of different texture feature extraction techniques in image retrieval systems
Multimedia Tools and Applications ( IF 3.0 ) Pub Date : 2020-07-24 , DOI: 10.1007/s11042-020-09317-3
Shefali Dhingra , Poonam Bansal

Content based image retrieval (CBIR) is an extrusive technique of retrieving the relevant images from vast image archives by extracting their low level features. In this research paper, the pursuance of five most prominent texture feature extraction techniques used in CBIR systems are experimentally compared in detail. The main issue with the CBIR systems is the proper selection of techniques for the extraction of low level features which comprises of color, texture and shape. Among these features, texture is one of the most decisive and dominant features. This selection of features completely depends upon the type of images to be retrieved from the database. The texture techniques explored here are Grey level co-occurrence matrix (GLCM), Discrete wavelet transform (DWT), Gabor transform, Curvelet and Local binary pattern (LBP). These are experimented on three touchstone databases which are Wang, Corel-5 K and Corel-10 K. The chief parameters of CBIR systems are evaluated here such as precision, recall and F-measure on all these databases using all the techniques. After detailed investigation it is figured out that LBP, GLCM and DWT provide highlighted and comparable results in all these datasets in terms of average precision. Besides practical implementation, the précised conceptual examination of these three texture techniques is also proposed in this article. So, this analysis is extremely beneficial for selecting the appropriate feature extraction technique by taking into consideration the experimental results along with image conditions such as noise, rotation etc.



中文翻译:

图像检索系统中不同纹理特征提取技术的实验类比

基于内容的图像检索(CBIR)是一种可提取的技术,可通过提取低层特征从大型图像档案中检索相关图像。在这篇研究论文中,对在CBIR系统中使用的五种最突出的纹理特征提取技术的追求进行了实验比较。CBIR系统的主要问题是正确选择用于提取包括颜色,纹理和形状在内的低级特征的技术。在这些特征中,纹理是最决定性和最主要的特征之一。功能的选择完全取决于要从数据库中检索的图像类型。此处探讨的纹理技术是灰度共生矩阵(GLCM),离散小波变换(DWT),Gabor变换,Curvelet和局部二进制图案(LBP)。这些在Wang,Corel-5 K和Corel-10 K的三个试金石数据库上进行了实验。在这里,使用所有技术对CBIR系统的主要参数(如精度,查全率和F量度)进行评估。经过详细调查,我们发现LBP,GLCM和DWT在所有这些数据集中以平均精度提供了突出且可比较的结果。除了实际的实现,本文还对这三种纹理技术提出了概念上的检验。因此,此分析通过考虑实验结果以及图像条件(例如噪声,旋转等),对于选择合适的特征提取技术极为有益。在这里,使用所有技术在所有这些数据库上评估CBIR系统的主要参数,例如精度,召回率和F量度。经过详细调查,我们发现LBP,GLCM和DWT在所有这些数据集中以平均精度提供了突出且可比较的结果。除了实际的实现,本文还对这三种纹理技术提出了概念上的检验。因此,此分析通过考虑实验结果以及图像条件(例如噪声,旋转等),对于选择合适的特征提取技术极为有益。在这里,使用所有技术在所有这些数据库上评估CBIR系统的主要参数,例如精度,召回率和F量度。经过详细调查,我们发现LBP,GLCM和DWT在所有这些数据集中以平均精度提供了突出且可比较的结果。除了实际的实现,本文还提出了对这三种纹理技术的概念性检查。因此,此分析通过考虑实验结果以及图像条件(例如噪声,旋转等),对于选择合适的特征提取技术极为有益。GLCM和DWT在所有这些数据集中以平均精度提供了突出且可比较的结果。除了实际的实现,本文还提出了对这三种纹理技术的概念性检查。因此,此分析通过考虑实验结果以及图像条件(例如噪声,旋转等),对于选择合适的特征提取技术极为有益。GLCM和DWT在所有这些数据集中以平均精度提供了突出且可比较的结果。除了实际的实现,本文还提出了对这三种纹理技术的概念性检查。因此,此分析通过考虑实验结果以及图像条件(例如噪声,旋转等),对于选择合适的特征提取技术极为有益。

更新日期:2020-07-25
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