UNSUPERVISED IMAGE CLASSIFICATION OF FISH WITHOUT THE INFERENCE OF CLUSTER NUMBER
Abstract
This thesis investigates deep learning techniques, particularly unsupervised image
classification, for identifying and clustering fish images captured with underwater
cameras. In collaboration with Innovasea, the goal is to streamline fish species identification
and reduce labor-intensive manual labeling of camera data at the White
Rock Dam test site in Nova Scotia, Canada. We developed an unsupervised clustering
framework based on the DeepDPM deep learning model. We first reproduced
DeepDPM results on several standard datasets. We then integrated ViT MAE embeddings
with DeepDPM and applied ESRGAN-based image processing to enhance
fish images, which are often blurry and low resolution. These techniques improved
clustering accuracy from 30% to 80% for five species of fish. However, using a cluster
visualization tool we developed, we observed that fish with similar appearances were
clustered together. Our results demonstrate progress towards automating fish species
classification and suggest future avenues of research towards this goal.