Ensemble learning has demonstrated its efficiency in many computer vision tasks. In this paper, we address this paradigm within content based image retrieval (CBIR). We propose to build an ensemble of convolutional neural networks (CNNs), either by training the CNNs on different bags of images, orby using CNNs trained on the same dataset, but having different architectures. Each network is used to extract the class probability vectors from images to use them as representations. The final image representation is then generated by combining the extracted class probability vectors from the built ensemble. We show that the use of CNN ensembles is very efficient in generating a powerful image representation compared to individual CNNs. Moreover, we propose an Averarge Query Expansion technique for our proposal to enhance the retrieval results. Several experiments were conducted to extensively evaluate the application of ensemble learning in CBIR. Results in terms of precision, recall, and mean average precision show the outperformance of our proposal compared to the state of the art.

中文翻译：

---

深度学习对象分类器集成的基于内容的图像检索

集成学习已证明其在许多计算机视觉任务中的效率。在本文中，我们解决了基于内容的图像检索（CBIR）中的这种范例。我们建议通过在不同图像包上训练CNN或使用在相同数据集上训练但具有不同体系结构的CNN来构建卷积神经网络（CNN）的集合。每个网络都用于从图像中提取类别概率向量，以将它们用作表示形式。然后，通过组合从构建的集合中提取的类别概率向量来生成最终的图像表示。我们显示，与单个CNN相比，使用CNN集成在生成强大的图像表示中非常有效。此外，对于我们的提议，我们提出了Averarge查询扩展技术，以增强检索结果。进行了一些实验，以广泛评估集成学习在CBIR中的应用。在精度，召回率和平均平均精度方面的结果表明，与现有技术相比，我们的提案表现出众。