关键词: 多示例学习;信息瓶颈聚类;高斯混合模型;期望最大化算法;场景分类;K-L散度

Abstract:

Multi-Instance learning (MIL) is a learning framework proposed recently, and has been successfully used in scene classification. First,A new image MultiInstance (MI) bag generating method was proposed, which models an image with a Gaussian Mixed Model (GMM). The generated GMM was treated as an MI bag and the components of the GMM were the instances of the corresponding bag. Then, the information bottleneck clustering was employed to transform the MIL problem into single-instance learning problem so that single-instance classifiers could be used for classification. Ensemble learning was involved to further enhance classifiers’ generalization ability. Experimental results showed that the proposed method was superior to some common MI algorithms on average in a 5-class scene classification task.

Key words: multi-instance learning; information bottleneck;  Gaussian mixed model; expectation maximization; scene classification; Kullback-Leibler divergence