Image retrieval is the process of searching and retrieving images from a database based on their visual content and features. Recently, much attention has been directed towards the retrieval of irregular patterns within industrial or medical images by extracting features from the images, such as deep features, colour-based features, shape-based features and local features. This has applications across a spectrum of industries, including fault inspection, disease diagnosis, and maintenance prediction. This paper proposes an image retrieval framework to search for images containing similar irregular patterns by extracting a set of morphological features (DefChars) from images; the datasets employed in this paper contain wind turbine blade images with defects, chest computerised tomography scans with COVID-19 infection, heatsink images with defects, and lake ice images. The proposed framework was evaluated with different feature extraction methods (DefChars, resized raw image, local binary pattern, and scale-invariant feature transforms) and distance metrics to determine the most efficient parameters in terms of retrieval performance across datasets. The retrieval results show that the proposed framework using the DefChars and the Manhattan distance metric achieves a mean average precision of 80% and a low standard deviation of 0.09 across classes of irregular patterns, outperforming alternative feature-metric combinations across all datasets. Furthermore, the low standard deviation between each class highlights DefChars' capability for a reliable image retrieval task, even in the presence of class imbalances or small-sized datasets.

翻译：图像检索是根据视觉内容和特征从数据库中搜索并检索图像的过程。近年来，通过提取深度特征、颜色特征、形状特征和局部特征等图像特征来检索工业或医学图像中的不规则模式备受关注，在缺陷检测、疾病诊断和维护预测等各行业均有应用。本文提出一种图像检索框架，通过从图像中提取一组形态学特征（DefChars）来搜索包含相似不规则模式的图像；本文使用的数据集包含带缺陷的风力涡轮机叶片图像、COVID-19感染的胸部计算机断层扫描图像、带缺陷的散热器图像以及湖冰图像。采用不同特征提取方法（DefChars、调整大小的原始图像、局部二值模式和尺度不变特征变换）及距离度量对框架进行评估，以确定各数据集上检索性能最有效的参数。检索结果显示，采用DefChars与曼哈顿距离度量的框架在不规则模式类别间平均精度均值达80%，标准差低至0.09，在所有数据集上均优于其他特征-度量组合。此外，各类别间的低标准差凸显了DefChars在类别不平衡或小规模数据集场景下实现可靠图像检索任务的能力。