Image clustering is a complex procedure, which is significantly affected by the choice of image representation. Most of the existing image clustering methods treat representation learning and clustering separately, which usually bring two problems. On the one hand, image representations are difficult to select and the learned representations are not suitable for clustering. On the other hand, they inevitably involve some clustering step, which may bring some error and hurt the clustering results. To tackle these problems, we present a new clustering method that efficiently builds an image representation and precisely discovers cluster assignments. For this purpose, the image clustering task is regarded as a binary pairwise classification problem with local structure preservation. Specifically, we propose here such an approach for image clustering based on a fully convolutional autoencoder and deep adaptive clustering (DAC). To extract the essential representation and maintain the local structure, a fully convolutional autoencoder is applied. To manipulate feature to clustering space and obtain a suitable image representation, the DAC algorithm participates in the training of autoencoder. Our method can learn an image representation that is suitable for clustering and discover the precise clustering label for each image. A series of real-world image clustering experiments verify the effectiveness of the proposed algorithm.

中文翻译：

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基于自动编码器和深度自适应聚类的图像表示学习

图像聚类是一个复杂的过程，受图像表示的选择影响很大。现有的大多数图像聚类方法都将表示学习和聚类分开对待，通常会带来两个问题。一方面，图像表示难以选择，并且学习到的表示不适合聚类。另一方面，它们不可避免地涉及一些聚类步骤，这可能带来一些错误并损害聚类结果。为了解决这些问题，我们提出了一种新的聚类方法，该方法可以有效地构建图像表示并精确地发现聚类分配。为此，将图像聚类任务视为具有局部结构保护的二进制成对分类问题。具体来说，我们在这里提出了一种基于全卷积自动编码器和深度自适应聚类（DAC）的图像聚类方法。为了提取基本表示并维持局部结构，应用了全卷积自动编码器。为了操纵特征以聚类空间并获得合适的图像表示，DAC算法参与了自动编码器的训练。我们的方法可以学习适合聚类的图像表示形式，并为每个图像发现精确的聚类标签。一系列真实世界的图像聚类实验证明了该算法的有效性。为了操纵特征以聚类空间并获得合适的图像表示，DAC算法参与了自动编码器的训练。我们的方法可以学习适合聚类的图像表示形式，并为每个图像发现精确的聚类标签。一系列真实世界的图像聚类实验证明了该算法的有效性。为了操纵特征以聚类空间并获得合适的图像表示，DAC算法参与了自动编码器的训练。我们的方法可以学习适合聚类的图像表示形式，并为每个图像发现精确的聚类标签。一系列真实世界的图像聚类实验证明了该算法的有效性。