Adaptive Hypergraph Learning and its Application in Image Classification
Adaptive Hypergraph Learning and its Application in Image Classification
复制标题
自适应超图学习及其在图像分类中的应用
DOI：
10.1109/tip.2012.2190083
复制
发表时间：
2012-07-01
影响因子：
10.6
通讯作者：
Wang, Meng
中科院分区：
文献类型：
--
作者：
Yu, Jun;Tao, Dacheng;Wang, Meng
近年来，人们对基于图的传导图像分类的兴趣激增。然而，现有的简单的基于图的转导学习方法仅对图像的成对关系进行建模，并且它们对相似性计算中使用的半径参数敏感。人们已经研究了超图学习来解决这两个问题。它通过使用超边链接多个样本来建模样本的高阶关系。然而，现有的超图学习方法面临两个问题，即如何生成超边以及如何处理大量超边。本文提出了一种用于传导图像分类的自适应超图学习方法。在我们的方法中，我们通过链接图像及其最近邻来生成超边缘。通过改变邻域的大小，我们能够为每个图像及其视觉邻域生成一组超边缘。我们的方法同时学习未标记图像的标签和超边的权重。通过这种方式，我们可以自动调节不同超边的效果。彻底的实证研究表明，与代表性基线相比，我们的方法是有效的。
Recent years have witnessed a surge of interest in graph-based transductive image classification. Existing simple graph-based transductive learning methods only model the pairwise relationship of images, however, and they are sensitive to the radius parameter used in similarity calculation. Hypergraph learning has been investigated to solve both difficulties. It models the high-order relationship of samples by using a hyperedge to link multiple samples. Nevertheless, the existing hypergraph learning methods face two problems, i.e., how to generate hyperedges and how to handle a large set of hyperedges. This paper proposes an adaptive hypergraph learning method for transductive image classification. In our method, we generate hyperedges by linking images and their nearest neighbors. By varying the size of the neighborhood, we are able to generate a set of hyperedges for each image and its visual neighbors. Our method simultaneously learns the labels of unlabeled images and the weights of hyperedges. In this way, we can automatically modulate the effects of different hyperedges. Thorough empirical studies show the effectiveness of our approach when compared with representative baselines.