基于稀疏编码和集成学习的多示例多标记图像分类方法

 该文基于稀疏编码和集成学习提出了一种新的多示例多标记图像分类方法。首先，利用训练包中所有示例学习一个字典，根据该字典计算示例的稀疏编码系数；然后基于每个包中所有示例的稀疏编码系数计算包特征向量，从而将多示例多标记问题转化为多标记问题；最后利用多标记分类算法进行求解。为了提高分类器的泛化能力，对多个分类器进行集成。在多示例多标记图像数据集上的实验结果表明所提方法与其它方法相比有更好的性能。

A Multi-instance Multi-label Image Classification Method Based on Sparse Coding and Ensemble Learning

This paper presents a novel multi-instance multi-label image classification method based on sparse coding and ensemble learning. First, a dictionary is learned based on all the instances in the training bags, and the sparse coding coefficient of each instance is calculated according to the dictionary; Second, a bag feature vector is computed based on all the sparse coding coefficients of the bag. Multi-instance multi-label issue is transformed into multi-label issue that can be solved by the multi-label algorithm. Ensemble learning is involved to enhance further the classifiers’ generalization. Experimental results on multi-instance multi-label image data show that the proposed method is superior to the state-of-art methods in terms of metrics.