  Despite significant advances in clustering methods in recent years, the
outcome of clustering of a natural image dataset is still unsatisfactory due to
two important drawbacks. Firstly, clustering of images needs a good feature
representation of an image and secondly, we need a robust method which can
discriminate these features for making them belonging to different clusters
such that intra-class variance is less and inter-class variance is high. Often
these two aspects are dealt with independently and thus the features are not
sufficient enough to partition the data meaningfully. In this paper, we propose
a method where we discover these features required for the separation of the
images using deep autoencoder. Our method learns the image representation
features automatically for the purpose of clustering and also select a coherent
image and an incoherent image simultaneously for a given image so that the
feature representation learning can learn better discriminative features for
grouping the similar images in a cluster and at the same time separating the
dissimilar images across clusters. Experiment results show that our method
produces significantly better result than the state-of-the-art methods and we
also show that our method is more generalized across different dataset without
using any pre-trained model like other existing methods.
