MIForests: Multiple-Instance Learning with Randomized Trees

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 object classification, detection and tracking. This paper presents a novel multipleinstance learning 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 training random forests and using a fast iterative homotopy method for solving the non-convex optimization problem. Additionally, most previously proposed MIL approaches operate in batch or off-line mode and thus assume access to the entire training set. This li...

• Type: Conference
• Year: 2010
• Where: ECCV
• Authors: Christian Leistner, Amir Saffari, Horst Bischof
• Keywords: Computer Vision, ECCV 2010, Fast Iterative Homotopy, Random Variables, Randomized Trees