  Recently, a common starting point for solving complex unsupervised image
classification tasks is to use generic features, extracted with deep
Convolutional Neural Networks (CNN) pretrained on a large and versatile dataset
(ImageNet). However, in most research, the CNN architecture for feature
extraction is chosen arbitrarily, without justification. This paper aims at
providing insight on the use of pretrained CNN features for image clustering
(IC). First, extensive experiments are conducted and show that, for a given
dataset, the choice of the CNN architecture for feature extraction has a huge
impact on the final clustering. These experiments also demonstrate that proper
extractor selection for a given IC task is difficult. To solve this issue, we
propose to rephrase the IC problem as a multi-view clustering (MVC) problem
that considers features extracted from different architectures as different
"views" of the same data. This approach is based on the assumption that
information contained in the different CNN may be complementary, even when
pretrained on the same data. We then propose a multi-input neural network
architecture that is trained end-to-end to solve the MVC problem effectively.
This approach is tested on nine natural image datasets, and produces
state-of-the-art results for IC.
