目的 在传统的词袋模型图像搜索问题中，许多工作致力于提高局部特征的辨识能力。图像搜索得到的图像在细节部分和查询图像相似，但是有时候这些图像在语义层面却差别很大。而基于全局特征的图像搜索在细节部分丢失了很多信息，致使布局相似实则不相关的图像被认为是相关图像。为了解决这个问题，本文利用深度卷积特征来构建一个动态匹配核函数。方法 利用这个动态匹配核函数，在鼓励相关图像之间产生匹配对的同时，抑制不相关图像之间匹配对的个数。该匹配核函数将图像在深度卷积神经网络全连接层最后一层特征作为输入，构建一个动态匹配核函数。对于相关图像，图像之间的局部特征匹配数量和质量都会相对增强。反之，对于不相关的图像，这个动态匹配核函数会在减少局部特征匹配的同时，降低其匹配得分。结果 从数量和质量上评估了提出的动态匹配核函数，提出了两个指标来量化匹配核函数的表现。基于这两个指标，本文对中间结果进行了分析，证实了动态匹配核函数相比于静态匹配核函数的优越性。最后，本文在5个公共数据集进行了大量的实验，在对各个数据集的检索工作中，得到的平均准确率从85.11%到98.08%，均高于此领域的同类工作。结论 实验结果表明了本文方法是有效的，并且其表现优于当前这一领域的同类工作。本文方法相比各种深度学习特征提取方法具有一定优势，由于本文方法使用特征用于构建动态匹配内核，而不是粗略编码进行相似性匹配，因此能在所有数据集上获得更好的性能。

Objective For the traditional image search retrieval problem based on the bag-of-words model, the enhancement of the recognition capability of local features of images has attracted considerable attention. Although the image results obtained by image search retrieval are similar to the query image in the detail part, the image results differ from the semantic perspective. In addition, an image search retrieval method based on global descriptors is used, which focuses on global features but loses several valuable information in the detail. Thus, the images that have similar layout but are not related are considered as related images. To solve this problem, this study uses deep convolution features to construct a dynamic matching kernel function. Method The proposed dynamic match kernel algorithm stimulates the feature matches between near-duplicate images and filters the matches between irrelevant images. This study extracts the features from the last fully connected layer in a convolutional neural network as the input for dynamic match kernel. Then, an adaptive threshold is constructed to match the local features. The threshold for relevant images should be large to enable the inclusion of positive matches. Conversely, for uncorrelated images, this dynamic matching kernel function reduces the matching score and local feature matching. Result In this study, we initially proposed two criteria to evaluate the effect of the dynamic match kernel algorithm and two indicators to quantify the performance of the dynamic matching kernel function. Then, on the basis of the two indicators, this study analyzed the intermediate results and verified the superiority of the dynamic matching kernel function through a comparison with the static matching kernel function. Finally, this study conducted a large number of experiments on five public datasets (Holidays, UKBench, Paris6K, Oxford5K, and DupImages), including the experiments with dynamic matching kernel function methods with other methods and experiments with dynamic matching kernel functions versus mainstream deep learning methods. The range of average accuracy is from 85.11% to 98.08% using our method, which indicates that the dynamic kernel method is compatible with other methods. Conclusion The dynamic kernel method can be used as a portable component of image retrieval to improve the experimental results of image search retrieval. In the contrast deep learning method, the experimental results of our dynamic matching method with other methods outperform the method based on deep learning features, which indicate that the proposed method is effective and its performance is better than the current work in this field.