Flower Classification and Recognition Based on Significance Test and Transfer Learning

Abstract

Aiming at the problem that traditional flower classification methods and ordinary convolutional neural networks are difficult to reduce the effect of flower background, the classification effect is not ideal. This paper designs a flower classification model that combines saliency detection and VGG-16 convolutional neural network, and adopts stochastic gradient descent algorithm and prevents over-fitting technology to improve the model. Experiments on the international public flower recognition data set Oxford flower-102 show that the model proposed in this paper is better than other traditional network models and has high recognition accuracy, robustness and generalization ability, which can classify flowers and have higher practical value accurately and quickly.