SPIE Medical Imaging 2013
This project and subsequent conference paper was the culmination of my work at Oak Ridge National Laboratory during the summer of 2012. It was a great experience and taught me a lot about research, programming, and the scientific process. Take a look at the paper or the presentation that I gave at the conference in Florida to learn more about the project.
Design Need and Idea This GUI was designed because current perception gathering techniques did not produce results that were reliably and excitingly accurate. This model allows users to cross-compare their choices as they work, meaning that users remain more reliably self-consistent across all of their ratings vs. other methods. Abstract: We present a novel graphical user interface (GUI) that facilitates high-efficacy collection of perceptual similarity opinions of a user in an effective and intuitive manner. The GUI is based on a hybrid mechanism that combines ranking and rating. Namely, it presents a base image for rating its similarity to seven peripheral images that are simultaneously displayed in a circular layout. The user is asked to report the base image’s pairwise similarity to each peripheral image on a fixed scale while preserving the relative ranking among all peripheral images. The collected data are then used to predict the user’s subjective opinions regarding the perceptual similarity of images. We tested this new approach against two methods commonly used in perceptual similarity studies: (1) a ranking method that presents triplets of images for selecting the image pair with the highest internal similarity and (2) a rating method that presents pairs of images for rating their relative similarity on a fixed scale. We aimed to determine which data collection method was the most time efficient and effective for predicting a user’s perceptual opinions regarding the similarity of mammographic masses. Our study was conducted with eight individuals. By using the proposed GUI, we were able to derive individual perceptual similarity profiles with a prediction accuracy ranging from 76.83% to 92.06% which was 41.4% to 46.9% more accurate than those derived with the other two data collection GUIs. The accuracy improvement was statistically significant. |
Downloadable Files
School article on my presentation | ||||
