Creating Images by Learning Image Semantics Using Vector Space Models

Authors

  • Derrall Heath Brigham Young University
  • Dan Ventura Brigham Young University

DOI:

https://doi.org/10.1609/aaai.v30i1.10149

Keywords:

Image Generation, Vector Space Models, Semantic Models

Abstract

When dealing with images and semantics, most computational systems attempt to automatically extract meaning from images. Here we attempt to go the other direction and autonomously create images that communicate concepts. We present an enhanced semantic model that is used to generate novel images that convey meaning. We employ a vector space model and a large corpus to learn vector representations of words and then train the semantic model to predict word vectors that could describe a given image. Once trained, the model autonomously guides the process of rendering images that convey particular concepts. A significant contribution is that, because of the semantic associations encoded in these word vectors, we can also render images that convey concepts on which the model was not explicitly trained. We evaluate the semantic model with an image clustering technique and demonstrate that the model is successful in creating images that communicate semantic relationships.

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Published

2016-02-21

How to Cite

Heath, D., & Ventura, D. (2016). Creating Images by Learning Image Semantics Using Vector Space Models. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.10149

Issue

Section

Technical Papers: Machine Learning Applications