Greedy Multiple Instance Learning via Codebook Learning and Nearest Neighbor Voting
This addresses the scalability issue for researchers and practitioners applying MIL to large datasets, though it is incremental as it builds on existing MIL frameworks.
The paper tackles the problem of slow multiple instance learning (MIL) algorithms by proposing a greedy strategy that uses density ratio modeling and codebook learning, achieving comparable accuracy to state-of-the-art methods while being at least 10 times faster on datasets like TRECVID MED11.
Multiple instance learning (MIL) has attracted great attention recently in machine learning community. However, most MIL algorithms are very slow and cannot be applied to large datasets. In this paper, we propose a greedy strategy to speed up the multiple instance learning process. Our contribution is two fold. First, we propose a density ratio model, and show that maximizing a density ratio function is the low bound of the DD model under certain conditions. Secondly, we make use of a histogram ratio between positive bags and negative bags to represent the density ratio function and find codebooks separately for positive bags and negative bags by a greedy strategy. For testing, we make use of a nearest neighbor strategy to classify new bags. We test our method on both small benchmark datasets and the large TRECVID MED11 dataset. The experimental results show that our method yields comparable accuracy to the current state of the art, while being up to at least one order of magnitude faster.