Multi-instance learning with an extended kernel density estimation for object categorization

Multi-instance learning (MIL) is a variational supervised learning. Instead of getting a set of instances that are labeled, the learner receives a set of bags that are labeled. Each bag contains many instances. In this paper, we present a novel MIL algorithm that can efficiently learn classifiers in a large instance space. We achieve this by estimating instance distribution using a proposed extended kernel density estimation (eKDE) which is an alternative to previous diverse density estimation (DDE). A fast method is devised to approximately locate the multiple modes of eKDE. Comparing to DDE, eKDE is more efficient and robust to the labeling noise (the mislabeled training data). We compare our approach with other state-of-the-art MIL methods in object categorization on the popular Caltech-4 and SIVAL datasets, the results illustrate that our approach provides superior performance.