NEURAL DISCRIMINANT ANALYSIS FOR FINE-GRAINED CLASSIFICATION

Feature learning is an essential process in image and object classification, even more so for fine-grained classification where objects have a similar global structure and subtle differences in local parts. Inspired by Linear Discriminant Analysis (LDA), we propose a two-phase optimization to transform deep features from their original space to a lower dimension space using neural networks with two primary goals: minimizing variances within each class and maximizing pairwise distances between features from different classes. The approach produces more discriminative features that lead to improvements in classification performance. We evaluate our method on four well-known fine-grained classification datasets. Our optimization leads to significantly better classification accuracy (up to 6.4% increase from the baseline). The standard deviations of the accuracy over multiple training runs show that our approach yields more consistent and reliable results than transfer learning.