In this study, the authors investigate content based image retrieval (CBIR) using ordered-dither block truncation coding (ODBTC) and phase congruency feature (PCF). Relevant feature extraction plays a vital role for retrieving the image in CBIR. The unique reason to choose PCF with ODBTC is that it detects the edges and corners during variation of image while preserving image brightness and contrast. Combining the PCF and ODBTC features improves CBIR system usage in various visual data processing domains. Thus, yields a better CBIR system which assists in the reduction of storage space, decreases retrieval time and increases accuracy of the system. The precision and recall are used as performance metrics to evaluate the proposed method based on retrieval of relevant images. Extensive experimental results with Corel 1 K (1000 images), Corel 10 K (10000 images) and CALTECH 256 (30144 images) proves that the proposed method is more desirable than antecedent proposed CBIR system in terms of accuracy, precision and recall.

中文翻译：

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相位一致性和基于ODBTC的图像检索

在这项研究中，作者研究了使用有序抖动块截断编码（ODBTC）和相位一致性功能（PCF）的基于内容的图像检索（CBIR）。相关特征提取对于在CBIR中检索图像起着至关重要的作用。选择带ODBTC的PCF的唯一原因是，它可以在图像变化期间检测边缘和角落，同时保留图像的亮度和对比度。结合PCF和ODBTC功能，可以提高CBIR系统在各种可视数据处理领域中的使用率。因此，产生了更好的CBIR系统，其有助于减少存储空间，减少检索时间并提高系统的准确性。精度和召回率用作性能指标，以基于相关图像的检索来评估所提出的方法。使用Corel 1 K的广泛实验结果（1000张图像），