Object-based Probabilistic Similarity Evidence of Sparse Latent Features
  from Fully Convolutional Networks
Cyril Juliani
Similarity analysis using neural networks has emerged as a powerful technique
for understanding and categorizing complex patterns in various domains. By
leveraging the latent representations learned by neural networks, data objects
such as images can be compared effectively. This research explores the
utilization of latent information generated by fully convolutional networks
(FCNs) in similarity analysis, notably to estimate the visual resemblance of
objects segmented in 2D pictures. To do this, the analytical scheme comprises
two steps: (1) extracting and transforming feature patterns per 2D object from
a trained FCN, and (2) identifying the most similar patterns through fuzzy
inference. The step (2) can be further enhanced by incorporating a weighting
scheme that considers the significance of latent variables in the analysis. The
results provide valuable insights into the benefits and challenges of employing
neural network-based similarity analysis for discerning data patterns
effectively.