View prediction using manifold learning in non-linear feature subspace

In this paper, we make use of a convolutional autoencoder to predict multiple unseen views of an object in the infrared domain. The dataset we use for this purpose is called ‘DSIAC-ATR Image database’ which has never been used before for view prediction in the non-linear feature subspace. Our method involves exploiting the underlying feature subspace – the manifold of the object - to predict an unseen view. We address a more challenging task of view prediction by working with greyscale images- the infrared images collected both during the day and night. We propose multiple architectures that not only predict how an object (a military vehicle in this case) will look like at a certain orientation but also learn to predict day or night infrared image and produce either as asked. We train our networks and show via experiments that the weights do not learn the geometry of transformation in the Euclidean space but rather in the Riemannian space. We explore the underlying feature subspace and observe that the networks learn the manifolds and thereby produce sharp, distinct and natural-looking images.