Abnormal Clustering and Cross Slicing Transformer for Insect Fine-Grained Image Classification

Insect fine-grained image classification is an application scenario in fine-grained image classification. It not only has the characteristics of small inter-class differences and large intra-class differences, but also has the difficulty that some categories have multiple life-stage forms, which makes the general fine-grained image classification model difficult to play a role in insect scenes. To this end, based on the Vision Transformer, we design a fine-grained classification network for insect images based on abnormal clustering and cross slicing, called ACCS-Trans. In the first stage of the model, we use segmentation and clustering operations to distinguish the special morphology of insects in those few-shot life stages. The model can avoid the interference of few-sample abnormal morphology on the class feature extraction of the current class during training. The second stage is the cross slicing module, which uses the anchor box of the image sample segmentation region in the first stage to cut the original sample image to form the main target image, which is used as the information supplement area of the original image. Finally, the image is divided into doubling patch groups by two vertical patch operations. In the third stage, we concatenate the multiplication patch group and input it into the Vision Transformer network for class feature extraction. We fully experiment with ACCS-Trans on two insect image datasets. Compared with the current mainstream fine-grained image classification models, ACCS- Trans achieves state-of-the-art effects on both datasets, and we do ablation experiments for each module. The effect of each module on our ACCS- Trans is analyzed. The excellent performance of our ACCS- Trans in insect scenes is verified in these experiments, these provide new ideas for the task of fine-grained classification of insect images.