Greedy Multiple Instance Learning via Codebook Learning and Nearest Neighbor Voting

TL;DR

This paper introduces a fast greedy multiple instance learning method that uses codebook learning and nearest neighbor voting, achieving comparable accuracy to existing methods but significantly reducing computational time.

Contribution

It proposes a novel density ratio model and a greedy codebook learning strategy for efficient MIL, enabling faster processing on large datasets.

Findings

Achieves comparable accuracy to state-of-the-art MIL methods.

Runs at least ten times faster than existing algorithms.

Effective on both small and large datasets like TRECVID MED11.

Abstract

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.