INRIA Grenoble, France.
IEEE Trans Pattern Anal Mach Intell. 2010 Jan;32(1):2-11. doi: 10.1109/TPAMI.2008.285.
This paper introduces the contextual dissimilarity measure, which significantly improves the accuracy of bag-of-features-based image search. Our measure takes into account the local distribution of the vectors and iteratively estimates distance update terms in the spirit of Sinkhorn's scaling algorithm, thereby modifying the neighborhood structure. Experimental results show that our approach gives significantly better results than a standard distance and outperforms the state of the art in terms of accuracy on the Nistér-Stewénius and Lola data sets. This paper also evaluates the impact of a large number of parameters, including the number of descriptors, the clustering method, the visual vocabulary size, and the distance measure. The optimal parameter choice is shown to be quite context-dependent. In particular, using a large number of descriptors is interesting only when using our dissimilarity measure. We have also evaluated two novel variants: multiple assignment and rank aggregation. They are shown to further improve accuracy at the cost of higher memory usage and lower efficiency.
本文介绍了上下文差异度量方法，该方法显著提高了基于特征袋的图像搜索的准确性。我们的度量方法考虑了向量的局部分布，并以 Sinkhorn 缩放算法的精神迭代估计距离更新项，从而修改了邻域结构。实验结果表明，与标准距离相比，我们的方法能显著提高准确率，在 Nistér-Stewénius 和 Lola 数据集上的准确率也优于最新技术。本文还评估了大量参数的影响，包括描述符的数量、聚类方法、视觉词汇表大小和距离度量。最优参数选择被证明是相当依赖上下文的。特别是，只有在使用我们的差异度量方法时，使用大量描述符才是有趣的。我们还评估了两种新的变体：多分配和排序聚合。它们被证明可以在更高的内存使用和更低的效率的代价下进一步提高准确性。
