Image classification methods based on plain Bayesian inference: an experiment from AGH

This research introduces an image classification method using plain Bayesian inference for identifying AGH. The method combines a pretrained VGG-11 model with a Support Vector Machine (SVM) classifier, enhanced by data augmentation to address class imbalance. The model integrates both image and textual data to improve classification accuracy. Experimental results show that the method achieves high accuracy and AUROC scores, demonstrating its effectiveness for AGH identification, with potential applications in ecological monitoring.