Abstract
Most image retrieval methods use global features that aggregate local distinctive patterns into a single representation. However, the aggregation process destroys the relative spatial information by considering orderless sets of local descriptors. We propose to integrate relative spatial information into the aggregation process by taking into account co-occurrences of local patterns in a tensor framework. The resulting signature called Improved Spatial Tensor Aggregation (ISTA) is able to reach state of the art performances on well known datasets such as Holidays, Oxford5k and Paris6k.
Abstract (translated)
大多数图像检索方法使用全局特征将局部特征模式聚合成单一表示。然而，聚合过程通过考虑无序集合的局部描述符破坏相对空间信息。我们建议通过考虑张量框架中局部模式的共现，将相对空间信息整合到聚合过程中。名为Improved Spatial Tensor Aggregation（ISTA）的结果签名能够在众所周知的数据集（如Holidays，Oxford5k和Paris6k）上达到最佳性能表现。
URL
https://arxiv.org/abs/1806.08991
