This paper addresses the construction of inverted index for large-scale image retrieval. The inverted index proposed by J. Sivic brings a significant acceleration by reducing distance computations with only a small fraction of the database. The state-of-the-art inverted indices aim to build finer partitions that produce a concise and accurate candidate list. However, partitioning in these frameworks is generally achieved by unsupervised clustering methods which ignore the semantic information of images. In this paper, we replace the clustering method with image classification, during the construction of codebook. We then propose a merging and splitting method to solve the problem that the number of partitions is unchangeable in the inverted semantic-index. Next, we combine our semantic-index with the product quantization (PQ) so as to alleviate the accuracy loss caused by PQ compression. Finally, we evaluate our model on large-scale image retrieval benchmarks. Experiment results demonstrate that our model can significantly improve the retrieval accuracy by generating high-quality candidate lists.
翻译:本文涉及用于大规模图像检索的反向索引的构建。 J. Sivic 提议的反向索引通过减少数据库中一小部分的距离计算而带来显著的加速。 最先进的反向索引旨在构建更细的分区, 产生一个简明和准确的候选名单。 但是, 这些框架中的分割一般是通过无视图像语义信息的不受监督的分组方法实现的。 在本文中, 我们用图像分类取代了组合方法。 在构建代码簿的过程中, 我们提出合并和分解方法, 以解决在垂直的语义索引中无法更改分区数的问题。 下一步, 我们将我们的语义索引与产品量化( PQ) 结合起来, 以便减轻 PQ 压缩造成的准确损失。 最后, 我们评估了大规模图像检索基准的模型。 实验结果显示, 我们的模式可以通过生成高质量的候选名单, 大大提高检索的准确性 。