Multiple View Generation and Classification of Mid-wave Infrared Images using Deep Learning

by   Maliha Arif, et al.

We propose a novel study of generating unseen arbitrary viewpoints for infrared imagery in the non-linear feature subspace . Current methods use synthetic images and often result in blurry and distorted outputs. Our approach on the contrary understands the semantic information in natural images and encapsulates it such that our predicted unseen views possess good 3D representations. We further explore the non-linear feature subspace and conclude that our network does not operate in the Euclidean subspace but rather in the Riemannian subspace. It does not learn the geometric transformation for predicting the position of the pixel in the new image but rather learns the manifold. To this end, we use t-SNE visualisations to conduct a detailed analysis of our network and perform classification of generated images as a low-shot learning task.


page 2

page 3


Creating High Resolution Images with a Latent Adversarial Generator

Generating realistic images is difficult, and many formulations for this...

Neural Manifold Clustering and Embedding

Given a union of non-linear manifolds, non-linear subspace clustering or...

Multi-view Deep Subspace Clustering Networks

Multi-view subspace clustering aims to discover the inherent structure b...

Non-Linear Temporal Subspace Representations for Activity Recognition

Representations that can compactly and effectively capture the temporal ...

Manifold Learning Benefits GANs

In this paper, we improve Generative Adversarial Networks by incorporati...

PixelSteganalysis: Pixel-wise Hidden Information Removal with Low Visual Degradation

It is difficult to detect and remove secret images that are hidden in na...