We consider the design of an image representation that embeds and aggregates
a set of local descriptors into a single vector. Popular representations of
this kind include the bag-of-visual-words, the Fisher vector and the VLAD. When
two such image representations are compared with the dot-product, the
image-to-image similarity can be interpreted as a match kernel. In match
kernels, one has to deal with interference, i.e. with the fact that even if two
descriptors are unrelated, their matching score may contribute to the overall
similarity.
We formalise this problem and propose two related solutions, both aimed at
equalising the individual contributions of the local descriptors in the final
representation. These methods modify the aggregation stage by including a set
of per-descriptor weights. They differ by the objective function that is
optimised to compute those weights. The first is a "democratisation" strategy
that aims at equalising the relative importance of each descriptor in the set
comparison metric. The second one involves equalising the match of a single
descriptor to the aggregated vector.
These concurrent methods give a substantial performance boost over the state
of the art in image search with short or mid-size vectors, as demonstrated by
our experiments on standard public image retrieval benchmarks.

提出了两种方法来解决局部特征点干扰问题，即通过在聚合阶段包含一组描述符权重来平等地处理每个本地描述符的个体贡献，并在标准公共图像检索基准测试中进行实验证明对于短向量或中向量的图像搜索，这两种方法都比对象方法表现更好。