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Image clustering is to group a set of images into disjoint clusters in a waythat images in the same cluster are more similar to each other than to those inother 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 withfeatures used for clustering, even if unsupervised coding based methods haveimproved the performances a lot. To reduce the effect of clustering features,we propose a feature-weak-relevant method for image clustering. The proposedmethod converts an unsupervised clustering process into an alternativeiterative process of unsupervised learning and transfer learning. Theclustering process firstly starts up from handcrafted features based imageclustering to estimate an initial label for every image, and secondly use aproposed sampling strategy to choose images with reliable labels to feed atransfer-learning model to learn representative features that can be used fornext round of unsupervised learning. In this manner, image clustering isiteratively optimized. What's more, the handcrafted features are used to bootup the clustering process, and just have a little effect on the finalperformance; therefore, the proposed method is feature-weak-relevant.Experimental results on six kinds of public available datasets show that theproposed method outperforms state of the art methods and depends less on theemployed features at the same time.

A Transfer Learning based Feature-Weak-Relevant Method for Image Clustering