基于异质信息融合的网络图像半监督学习方法

网络图像通常包含文本、颜色和纹理等异质信息. 本文提出了一种基于多类异质信息融合的网络图像半监督学习方法---局部协同训练(Local co-training, LCT). 该方法在每个视图(对应一类 信息)上对每个样本点的邻域构建线性局部模型, 利用一组局部模型来表示数据关系;基于信息传播和协同训练对模型进行增量式迭代更新. 该算法在协同训练和基于图正则化的方法这两类半监督学习算法间建立了桥梁. 局部协同训练算法能够准确地描述样本的复杂分布, 并且可以进行高效的增量学习, 有利于大规模网络图像的在线学习. 在Corel, Pascal和ImageNet数据集上的实验结果表明该方法具有良好的性能.

Web Image Semi-supervised Learning Method Based on Heterogeneous Information Fusion

Web images generally consist of heterogeneous information including texts, colors and textures. This paper proposes a new method, called local co-training (LCT), for semi-supervised classification of web images based on fusion of heterogeneous information. The proposed method employs a set of local linear models to represent data points of each view, and incrementally refines these models by exploiting unlabeled data with information propagation and co-training. The local co-training builds a bridge between graph-based methods and co-training. The local co-training can model the instance distribution accurately in the high-dimensional space, and learn local models incrementally, which benefits the online classification of large scale of web images. Experiments on Corel, Pascal and ImageNet datasets demonstrate that the local co-training can effectively improve the classification performance of learners by exploiting multiple attribute sets and unlabeled data.