FGDCC: Fine-Grained Deep Cluster Categorization -- A Framework for Intra-Class Variability Problems in Plant Classification

TL;DR

This paper introduces FGDCC, a novel framework that uses class-wise clustering to learn fine-grained features, improving plant classification by addressing intra-class variability in FGVC tasks.

Contribution

The paper proposes a new hierarchical classification method leveraging clustering for intra-class variability in plant classification, achieving state-of-the-art results on PlantNet300k.

Findings

Achieved state-of-the-art performance on PlantNet300k dataset.

Clustering-based pseudo-labels help mitigate intra-class variability.

Initial experiments highlight potential for further optimization.

Abstract

Intra-class variability is given according to the significance in the degree of dissimilarity between images within a class. In that sense, depending on its intensity, intra-class variability can hinder the learning process for DL models, specially when such classes are also underrepresented, which is a very common scenario in Fine-Grained Visual Categorization (FGVC) tasks. This paper proposes a novel method that aims at leveraging classification performance in FGVC tasks by learning fine-grained features via classification of class-wise cluster assignments. Our goal is to apply clustering over each class individually, which can allow to discover pseudo-labels that encodes a latent degree of similarity between images. In turn, those labels can be employed in a hierarchical classification process that allows to learn more fine-grained visual features and thereby mitigating intra-class variability issues. Initial experiments over the PlantNet300k enabled to shed light upon several key points in which future work will have to be developed in order to find more conclusive evidence regarding the effectiveness of our method. Our method still achieves state-of-the-art performance on the PlantNet300k dataset even though some of its components haven't been shown to be fully optimized. Our code is available at \href{https://github.com/ADAM-UEFS/FGDCC}{https://github.com/ADAM-UEFS/FGDCC}.