Abstract: Aiming at the problem of object-based image retrieval, a novel algorithm named KP-MIL was proposed, which worked in the Multiple Instance Learning (MIL) framework. Firstly, this algorithm clustered the instances in positive set and negative set, and found the potential positive instance and bag structure. Then an alpha coefficient was introduced to trade off between positive instance and bags similarity. Experiments on SIGVAL dataset show that this algorithm is feasible, and the performance is superior to other MIL algorithms.

Key words: image retrieval, Multiple Instance Learning (MIL), K-means clustering, Radial Basis Function (RBF), alpha coefficient

摘要： 针对基于对象的图像检索问题，利用K均值（K-means）聚类，提出了一种新的基于多示例学习（MIL）框架的图像检索算法KP-MIL。该算法在正包和负包组成示例集合聚类，获取潜在正示例代表和包结构特性数据，然后利用径向基核分别度量两者的相似性，最后利用alpha因子均衡两者相似性对核函数结果的影响。在标准对象图像检索集SIGVAL上进行实验，实验结果表明，该方法是有效的且性能优于其他同类方法。

关键词: 图像检索, 多示例学习, K均值聚类, 径向基核, alpha因子