Hashing Multi-Instance Data from Bag and Instance Level

In many scenarios, we need to do similarity search of multi-instance data. Although the traditional kernel methods can measure the similarity of bags in original feature space, the time and storage cost of these methods are so high which makes such methods cannot deal with large scale problems. Recently, hashing methods have been widely used for similarity search due to its fast search speed and low storage cost. However, few works consider how to hash multi-instance data. In this paper, we present two multi-instance hashing methods: (1) Bag-level Multi-Instance Hashing (BMIH); (2) Instance-level Multi-Instance Hashing (IMIH). BMIH first maps each bag to a new feature representation by a feature fusion method; then, supervised hashing method is used to convert new features to hash code. To utilize more instance information in each bag, IMIH regards instances in all bags as training data and apply two types of hash learning methods (unsupervised and supervised, respectively) to convert all instances to binary code; then, for a test bag, a similarity measure is proposed to search similar bags. Our experiments on four real-world datasets show that instance-level hashing with supervised information outperforms all proposed techniques.