Abstract

Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many applications such as ob ject classification, detection and tracking. This paper presents a novel multiple-instance learn- ing algorithm for randomized trees called MIForests. Randomized trees are fast, inherently parallel and multi-class and are thus increasingly popular in computer vision. MIForest combine the advantages of these classifiers with the flexibility of multiple instance learning. In order to leverage the randomized trees for MIL, we define the hidden class labels inside target bags as random variables. These random variables are optimized by train- ing random forests and using a fast iterative homotopy method for solving the non-convex optimization problem. Additionally, most previously pro- posed MIL approaches operate in batch or off-line mode and thus assume access to the entire training set. This limits their applicability in scenarios where the data arrives sequentially and in dynamic environments. We show that MIForests are not limited to off-line problems and present an on-line extension of our approach. In the experiments, we evaluate MIForests on standard visual MIL benchmark datasets where we achieve state-of-the- art results while being faster than previous approaches and being able to inherently solve multi-class problems. The on-line version of MIForests is evaluated on visual ob ject tracking where we outperform the state-of-the- art method based on boosting.