Student Posters Repository

Image processing of faces and topic modeling of text are both widely used forms of processing employed in machine learning applications. Facial features can be used to identify faces that look similar or in an ideal case, a specific person, depending on the quality of the image. Topic modeling of text articles is employed across a variety of applications to improve content search from news or research articles, and is also used in a variety of natural language processing applications such as chat bots. The purpose of my research is to identify and validate candidate methods for linking textual articles and images they contain for assessing previously unseen images and/or articles. This requires that I transform both target data types (images and articles) into their respective n-dimensional spaces. I will use both convolutional neural nets (CNNs) and self-organizing maps (SOM) to help me encode the different instances (image, article) and store them for indexing. Data size and computational intensity require HPC for this to be successful. My research also explores identification of a common n-dimensional functional space where vectorized images and text can exist together, enabling classification of new articles and images based on the trained article and image pairs.