The great success of visual features learned from deep neural networks has led to a significant effort to develop efficient and scalable technologies for image retrieval. Nevertheless, its usage in large-scale Web applications of content-based retrieval is still challenged by their high dimensionality. To overcome this issue, some image retrieval systems employ the product quantization method to learn a large-scale visual dictionary from a training set of global neural network features. These approaches are implemented in main memory, preventing their usage in big-data applications. The contribution of the work is mainly devoted to investigating some approaches to transform neural network features into text forms suitable for being indexed by a standard full-text retrieval engine such as Elasticsearch. The basic idea of our approaches relies on a transformation of neural network features with the twofold aim of promoting the sparsity without the need of unsupervised pre-training. We validate our approach on a recent convolutional neural network feature, namely Regional Maximum Activations of Convolutions (R-MAC), which is a state-of-art descriptor for image retrieval. Its effectiveness has been proved through several instance-level retrieval benchmarks. An extensive experimental evaluation conducted on the standard benchmarks shows the effectiveness and efficiency of the proposed approach and how it compares to state-of-the-art main-memory indexes.

从深度神经网络学到的视觉特征的巨大成功，导致了为开发有效且可扩展的图像检索技术而付出的巨大努力。尽管如此，基于内容的检索在大规模Web应用程序中的使用仍然受到其高维度的挑战。为了克服这个问题，一些图像检索系统采用乘积量化方法从全局神经网络功能训练集中学习大规模可视词典。这些方法在主内存中实现，从而阻止了它们在大数据应用程序中的使用。这项工作的贡献主要致力于研究将神经网络特征转换为适合由标准全文检索引擎（例如Elasticsearch）索引的文本形式的一些方法。我们方法的基本思想依赖于神经网络特征的转换，其双重目的是在不需要无监督的预训练的情况下提高稀疏度。我们在最近的卷积神经网络功能上验证了我们的方法，即卷积的区域最大激活（R-MAC），它是图像检索的最新描述符。它的有效性已通过几个实例级别的检索基准得到了证明。在标准基准上进行的广泛实验评估表明，该方法的有效性和效率以及如何与最新的主内存索引进行比较。我们在最近的卷积神经网络功能上验证了我们的方法，即卷积的区域最大激活（R-MAC），它是图像检索的最新描述符。它的有效性已通过几个实例级别的检索基准得到了证明。在标准基准上进行的广泛实验评估显示了该方法的有效性和效率，以及如何与最新的主内存索引进行比较。我们在最近的卷积神经网络功能上验证了我们的方法，即卷积的区域最大激活（R-MAC），它是图像检索的最新描述符。它的有效性已通过几个实例级别的检索基准得到了证明。在标准基准上进行的广泛实验评估表明，该方法的有效性和效率以及如何与最新的主内存索引进行比较。