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Multiple-instance learning (MIL) has served as an important tool for a widerange of vision applications, for instance, image classification, objectdetection, and visual tracking. In this paper, we propose a novel method tosolve the classical MIL problem, named relaxed multiple-instance SVM (RMI-SVM).We treat the positiveness of instance as a continuous variable, use Noisy-ORmodel to enforce the MIL constraints, and jointly optimize the bag label andinstance label in a unified framework. The optimization problem can beefficiently solved using stochastic gradient decent. The extensive experimentsdemonstrate that RMI-SVM consistently achieves superior performance on variousbenchmarks for MIL. Moreover, we simply applied RMI-SVM to a challenging visiontask, common object discovery. The state-of-the-art results of object discoveryon Pascal VOC datasets further confirm the advantages of the proposed method.

Relaxed Multiple-Instance SVM with Application to Object Discovery