  Multiple-instance learning (MIL) has served as an important tool for a wide
range of vision applications, for instance, image classification, object
detection, and visual tracking. In this paper, we propose a novel method to
solve the classical MIL problem, named relaxed multiple-instance SVM (RMI-SVM).
We treat the positiveness of instance as a continuous variable, use Noisy-OR
model to enforce the MIL constraints, and jointly optimize the bag label and
instance label in a unified framework. The optimization problem can be
efficiently solved using stochastic gradient decent. The extensive experiments
demonstrate that RMI-SVM consistently achieves superior performance on various
benchmarks for MIL. Moreover, we simply applied RMI-SVM to a challenging vision
task, common object discovery. The state-of-the-art results of object discovery
on Pascal VOC datasets further confirm the advantages of the proposed method.
