  Image clustering is to group a set of images into disjoint clusters in a way
that images in the same cluster are more similar to each other than to those in
other clusters, which is an unsupervised or semi-supervised learning process.
It is a crucial and challenging task in machine learning and computer vision.
The performances of existing image clustering methods have close relations with
features used for clustering, even if unsupervised coding based methods have
improved the performances a lot. To reduce the effect of clustering features,
we propose a feature-weak-relevant method for image clustering. The proposed
method converts an unsupervised clustering process into an alternative
iterative process of unsupervised learning and transfer learning. The
clustering process firstly starts up from handcrafted features based image
clustering to estimate an initial label for every image, and secondly use a
proposed sampling strategy to choose images with reliable labels to feed a
transfer-learning model to learn representative features that can be used for
next round of unsupervised learning. In this manner, image clustering is
iteratively optimized. What's more, the handcrafted features are used to boot
up the clustering process, and just have a little effect on the final
performance; therefore, the proposed method is feature-weak-relevant.
Experimental results on six kinds of public available datasets show that the
proposed method outperforms state of the art methods and depends less on the
employed features at the same time.
