Fine-Grained Image Recognition Method Based on Input Perception Joint Probability Prediction
Xiaoxi Yuan · 2023
A method of fine-grained image recognition in the simple background based on input perception joint probability prediction is proposed. The recognition network consists of an input perception module and a joint uncertainty estimation module. The input perception module takes the input image as the global scale image and automatically extracts the foreground scale image and key component scale image by introducing the attention mechanism and using the foreground perception mechanism and component perception mechanism. The joint uncertainty estimation module uses three dynamic probability estimation parameters to evaluate the prediction confidence of each branch and then combines three branch votes to make the final decision. In order to verify the effectiveness and rationality of the input-aware and Probabilistic Prediction Convolutional Neural Network (IAPP-CNN) algorithm, a comparative experiment was performed on the data set. Ablation experiments verified the feasibility and rationality of the algorithm. The results show that the value of each evaluation index of the algorithm is more than 99.0%, which has high recognition accuracy and robustness and meets the recognition requirements. IAPP-CNN algorithm can improve the recognition accuracy of bird images with excellent performance and can greatly improve the performance of fine-grained image recognition.