IEEE Trans Image Process. 2018 Sep;27(9):4345-4356. doi: 10.1109/TIP.2018.2831454.
Utilizing multiple descriptions/views of an object is often useful in image clustering tasks. Despite many works that have been proposed to effectively cluster multi-view data, there are still unaddressed problems such as the errors introduced by the traditional spectral-based clustering methods due to the two disjoint stages: 1) eigendecomposition and 2) the discretization of new representations. In this paper, we propose a unified clustering framework which jointly learns the two stages together as well as utilizing multiple descriptions of the data. More specifically, two learning methods from this framework are proposed: 1) through a graph construction from different views and 2) through combining multiple graphs. Furthermore, benefiting from the separability and local graph preserving properties of the proposed methods, a novel unsupervised automatic attribute discovery method is proposed. We validate the efficacy of our methods on five data sets, showing that the proposed joint learning clustering methods outperform the recent state-of-the-art methods. We also show that it is possible to derive a novel method to address the unsupervised automatic attribute discovery tasks.
在图像聚类任务中，通常利用对象的多个描述/视图是很有用的。尽管已经提出了许多有效的多视图数据聚类方法，但仍存在一些未解决的问题，例如由于传统基于谱的聚类方法存在两个不相交的阶段：1）特征分解和 2）新表示的离散化而引入的错误。在本文中，我们提出了一个统一的聚类框架，该框架可以同时学习这两个阶段，并利用数据的多个描述。更具体地说，从这个框架中提出了两种学习方法：1）通过来自不同视图的图构建，以及 2）通过组合多个图。此外，受益于所提出方法的可分离性和局部图保持特性，提出了一种新颖的无监督自动属性发现方法。我们在五个数据集上验证了我们方法的有效性，结果表明所提出的联合学习聚类方法优于最新的最先进方法。我们还表明，有可能衍生出一种新的方法来解决无监督自动属性发现任务。
