Multiple Instance Learning for bags with Ordered instances

Date of Submission: 
June 7, 2017
Report Number: 
Report PDF: 

Multiple Instance Learning (MIL) algorithms are designed for problems where labels are available for groups of instances, commonly referred to as bags. In this paper, we consider a new MIL prob- lem setting where instances in a bag are not ex- changeable, and a bijection exists between every pair of bags. We propose a neural network based MIL algorithm (MILOrd) that leverages the exis- tence of such a bijection when learning to discrim- inate bags. MILOrd has an input node for each in- stance in the bag, an output node that captures the bag level prediction, and a hidden layer that cap- tures the output from an instance level classifier for each instance in the bag. The bag level prediction is obtained by combining these hidden layer val- ues using a function that models the importance of each instance, unlike the traditional schemes where each instance is considered equal. We demonstrate the utility of the proposed algorithm on the prob- lem of burned area mapping using yearly bags com- posed of multispectral reflectance data for different time steps in the year. Our experiments show that MILOrd outperforms traditional MIL schemes that don’t account for the presence of a bijection.