Multi-image aggregation for better visual object retrieval

We study how aggregating multiple images, on query or database side, impacts the performance of visual object retrieval in a Bag-of-Words framework. To this end, we first compare five different multi-image aggregation methods, and suggest selecting the average pooling method in most cases for its superior advantages in accuracy, speed, and memory footprint. Then we prove with experiments that more images generally yield better retrieval performance. What is more, we illustrate that simply aggregating query images without selection is far from optimal. Comprehensive experiments were conducted on three large-scale object retrieval datasets, and the new state-of the-art was achieved. This research can be leveraged in some real applications such as mobile search, where the retrieval performance will be improved once users snap multiple query images.