The amount of image content on the Internet has increased dramatically in recent years; its precise search and retrieval is a challenge at present. The methods that have shown high efficiency are those based on convolution neural networks (CNN) and, particularly, binary coding methods based on hashing functions. This article presents a new image retrieval scheme based on attributes from a CNN, an efficient low-dimensional binary auto-encoder, and, finally, a near-neighbor retrieval stage. The proposed methodology was tested with two image datasets CIFAR-10 and MNIST. The results are compared with existing methods in the literature.

中文翻译：

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基于二进制自动编码器和卷积神经网络的图像检索系统


近年来，互联网上的图像内容数量急剧增加；其精确的搜索和检索是目前的一个挑战。显示出高效率的方法是基于卷积神经网络（CNN）的方法，特别是基于散列函数的二进制编码方法。本文提出了一种基于 CNN 属性的新图像检索方案、一种高效的低维二进制自动编码器以及最后的近邻检索阶段。使用两个图像数据集 CIFAR-10 和 MNIST 测试了所提出的方法。结果与文献中现有方法进行了比较。