The Bag-of-Words (BoW) representation is well applied to recent
state-of-the-art image retrieval works. Typically, multiple vocabularies are
generated to correct quantization artifacts and improve recall. However, this
routine is corrupted by vocabulary correlation, i.e., overlapping among
different vocabularies. Vocabulary correlation leads to an over-counting of the
indexed features in the overlapped area, or the intersection set, thus
compromising the retrieval accuracy. In order to address the correlation
problem while preserve the benefit of high recall, this paper proposes a Bayes
merging approach to down-weight the indexed features in the intersection set.
Through explicitly modeling the correlation problem in a probabilistic view, a
joint similarity on both image- and feature-level is estimated for the indexed
features in the intersection set.
We evaluate our method through extensive experiments on three benchmark
datasets. Albeit simple, Bayes merging can be well applied in various merging
tasks, and consistently improves the baselines on multi-vocabulary merging.
Moreover, Bayes merging is efficient in terms of both time and memory cost, and
yields competitive performance compared with the state-of-the-art methods.

本文提出了一种贝叶斯合并方法来解决多词汇表中的词汇相关问题，并通过基于图像和特征级别的联合相似度评估其有效性。经过广泛的实验，该方法证明了在多词汇表合并中的竞争性性能。