A Guided Gaussian-Dirichlet Random Field for Scientist-in-the-Loop Inference in Underwater Robotics

Chad Samuelson, Joshua G Mangelson

Abstract:

Visual topic modeling (VTM) provides key insight
into data sets based on learned semantic topic models. The
Gaussian-Dirichlet Random Field (GDRF), a state-of-the-art
VTM technique, models these semantic topics in continuous space as densities. However, ambiguity in learned topics
is a disadvantage of such Dirichlet-based VTM algorithms.
We propose the Guided Gaussian-Dirichlet Random Field
(GGDRF). Our method applies Dirichlet Forest priors from
natural language processing (NLP) to the vision domain as a
way to embed visual scientific knowledge into the estimation
process. This modification and addition to the GDRF provides
a key shift from unsupervised machine learning to semisupervised machine learning in the robotic VTM domain. We
show through simulation and real-world underwater data that
the proposed GGDRF outperforms the previous GDRF method
both quantitatively and qualitatively by improving alignment
between estimated topics and scientific interests.