# A Self-Supervised Tree-Structured Framework for Fine-Grained Classification
###### Abstract  
In computer vision, fine-grained classification has become an important issue in recognizing objects with slight visual differences. Usually, it is challenging to generate good performance when solving fine-grained classification problems using traditional convolutional neural networks. To improve the accuracy and training time of convolutional neural networks in solving fine-grained classification problems, this paper proposes a tree-structured framework by eliminating the effect of differences between clusters. The contributions of the proposed method include the following three aspects: (1) a self-supervised method that automatically creates a classification tree, eliminating the need for manual labeling; (2) a machine-learning matcher which determines the cluster to which an item belongs, minimizing the impact of inter-cluster variations on classification; and (3) a pruning criterion which filters the tree-structured classifier, retaining only the models with superior classification performance. The experimental evaluation of the proposed tree-structured framework demonstrates its effectiveness in reducing training time and improving the accuracy of fine-grained classification across various datasets in comparison with conventional convolutional neural network models. Specifically, for the CUB 200 2011, FGVC aircraft, and Stanford car datasets, the proposed method achieves a reduction in training time of 32.91%, 35.87%, and 14.48%, and improves the accuracy of fine-grained classification by 1.17%, 2.01%, and 0.59%, respectively. f [topsep=0 pt] _Applied sciences_  
**Citation:** [PERSON]; [PERSON]. Moreover, solving fine-grained classification problems using traditional CNNs can be challenging. Extracted features often correlate with the image quality and the similarity of samples in the dataset, leading to suboptimal performance. To improve classification accuracy, many researchers focus on removing image noise, such as the background, to enhance image quality [8]. In this paper, we propose a novel framework for fine-grained classification that eliminates the effect of cluster similarity, which is distinct from traditional noise elimination methods. A dataset may contain different clusters, such as warplanes and passenger planes. However, when all samples (subclasses from various clusters) are combined for classification, a CNN classifier must differentiate not only the cluster but also the subclass. This is a complex task because the correlation between different clusters may affect the extracted features, leading to an inaccurate classification. To address this issue, it is essential to identify the belonging cluster for each subclass before the classification. For example, the 757-200 aircraft shown in Figure 1 should be classified into its belonging cluster (i.e., passenger planes) before further classification. This paper advances a novel tree-structured framework with the objective of enhancing classification accuracy and mitigating training time in comparison to CNNs for fine-grained classification tasks. The proposed framework determines the belonging cluster before classification, which can help to eliminate the effect of the similarity between different clusters. The paper makes several contributions: (1) a self-supervised method that can automatically construct an initial classification tree based on image features, which distinguishes the belonging cluster and eliminates the need for manual labeling; (2) a normal machine-learning matcher which acts as a classifier that utilizes machine-learning methods to assign objects to the belonging clusters, thus minimizing the impact of clusters on the classification process; and (3) a pruning criterion is proposed to filter the classifier with better classification performance, thereby reducing the computational cost. The experimental results demonstrate that the proposed tree-structured classifier improves the accuracy and training time of fine-grained classification compared to traditional CNN classifiers. The paper is organized as follows.