Flower image classification based on improved inception V4 network

Abstract

Flower classification is a fundamental work in the field of botany. Since flower images are fine-grained images with large intra-class differences and high inter-class similarity, it brings great challenges to their classification. With the rapid development of artificial intelligence technology, machine learning algorithms based on convolutional neural networks have begun to gradually replace manual methods for image recognition and classification tasks. When the traditional convolutional neural network model is applied to the task of flower image classification, the convolution with deep layers leads to the gradual weakening of spatial detail information, which makes it difficult for the feature map used to guide the classification results to fully express the fine-grained features of flowers. Therefore, the classification effect is not ideal. In order to improve the accuracy of flower image classification, a classification method based on the improved Inception V4 network is proposed. In the basic feature extraction stage, the shallow features are fused to obtain basic fusion features with more detailed spatial information. The base fusion features are then weighted by channel attention using multi-scale features. Finally, the weighted basic fusion features and the corresponding elements of the original multi-scale features are added and fused to form advanced fusion features for classification tasks. The experimental results show that the proposed improved Inception V4 network has a more ideal classification effect for flower images.