Image Corpus Representative Summarization

We propose a novel approach for image corpus representative summarization using GAN. Our technique can be used to automatically provide a condensed set of representatives for the given image collection. The generated summary can be used for rapid prototyping as models can be trained using the summarized set instead of the larger original dataset. The problem is challenging because a good summary must cover various aspects of an image set such as relevance and diversity. Additionally, lack of sufficient ground truth data makes the problem hard to solve using classical supervised machine learning approaches. In our algorithm, we use CNN and an MLP score layer to compute the priority of each image towards the summary. Our network is trained in an unsupervised manner using a generator for reconstructing the original dataset, and a discriminator, for classifying between original and summary. We show the efficacy of the algorithm using rigorous experiments.