During the last years, the problem of Content-Based Image Retrieval (CBIR) was addressed in many different ways, achieving excellent results in small-scale datasets. With growth of the data to evaluate, new issues need to be considered and new techniques are necessary in order to create an efficient yet accurate system. In particular, computational time and memory occupancy need to be kept as low as possible, whilst the retrieval accuracy has to be preserved as much as possible. For this reason, a brute-force approach is no longer feasible, and an Approximate Nearest Neighbor (ANN) search method is preferable. This paper describes the state-of-the-art ANN methods, with a particular focus on indexing systems, and proposes a new ANN technique called Bag of Indexes (BoI). This new technique is compared with the state of the art on several public benchmarks, obtaining 86.09% of accuracy on Holidays+Flickr1M, 99.20% on SIFT1M and 92.4% on GIST1M. Noteworthy, these state-of-the-art accuracy results are obtained by the proposed approach with a very low retrieval time, making it excellent in the trade off between accuracy and efficiency.

在过去的几年中，以多种方式解决了基于内容的图像检索（CBIR）问题，从而在小规模数据集中取得了优异的成绩。随着要评估的数据的增长，需要考虑新问题，并且有必要使用新技术来创建高效而准确的系统。特别地，需要将计算时间和存储占用率保持在尽可能低的水平，而必须尽可能地保持检索精度。由于这个原因，蛮力方法不再可行，并且最好采用近似最近邻（ANN）搜索方法。本文介绍了最新的ANN方法，特别着重于索引系统，并提出了一种称为索引袋（BoI）的新ANN技术。这项新技术在几个公共基准上与最新技术进行了比较，在Holidays + Flickr1M上的准确性为86.09％，在SIFT1M上的准确性为99.20％，在GIST1M上的准确性为92.4％。值得注意的是，这些最新的精度结果是通过提出的方法以极短的检索时间获得的，从而使其在精度和效率之间取得了很好的平衡。