2015/12/03 by Hanqiang Song, Song, Hanqiang, Zhuotun Zhu +3
Computer Science · Mathematics · #Advanced Image and Video Retrieval Techniques #FOS: Computer and information sciences #Image Retrieval and Classification Techniques #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Video Analysis and Summarization #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1512.00994
arxiv created 2015/12/03 · openalex publication_date 2015/12/03 · arxiv updated 2015/12/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Multi-instance learning (MIL) has a wide range of applications due to its distinctive characteristics. Although many state-of-the-art algorithms have achieved decent performances, a plurality of existing methods solve the problem only in instance level rather than excavating relations among bags. In this paper, we propose an efficient algorithm to describe each bag by a corresponding feature vector via comparing it with other bags. In other words, the crucial information of a bag is extracted from the similarity between that bag and other reference bags. In addition, we apply extensions of Hausdorff distance to representing the similarity, to a certain extent, overcoming the key challenge of MIL problem, the ambiguity of instances' labels in positive bags. Experimental results on benchmarks and text categorization tasks show that the proposed method outperforms the previous state-of-the-art by a large margin.