A Discriminative Data-Dependent Mixture-Model Approach for Multiple Instance Learning in Image Classification
Multiple Instance Learning (MIL) has been widely used in various applications including image classification. However, existing MIL methods do not explicitly address the multi-target problem where the distributions of positive instances are likely to be multi-modal. This strongly limits the performance of multiple instance learning in many real world applications. To address this problem, this paper proposes a novel discriminative data-dependent mixture-model method for multiple instance learning (MM-MIL) approach in image classification. The new method explicitly handles the multi-target problem by introducing a data-dependent mixture model, which allows positive instances to come from different clusters in a flexible manner. Furthermore, the kernelized representation of the proposed model allows effective and efficient learning in high dimensional feature space. An extensive set of experimental results demonstrate that the proposed new MM-MIL approach substantially outperforms several state-of-art MIL algorithms on benchmark datasets.
MIL, multiple instance learning, image classification, multi-model, data-dependent mixture model, high dimensional feature space, algorithms
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