Flower image classification based on improved inception V4 network

Jiwei Hu; Qinru Li

doi:10.1117/12.2639679

19 October 2022 Flower image classification based on improved inception V4 network

Jiwei Hu, Qinru Li

Proceedings Volume 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering; 122944K (2022) https://doi.org/10.1117/12.2639679
Event: 7th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2022), 2022, Xishuangbanna, China

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.

Citation Download Citation

Jiwei Hu and Qinru Li "Flower image classification based on improved inception V4 network", Proc. SPIE 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, 122944K (19 October 2022); https://doi.org/10.1117/12.2639679

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KEYWORDS

Image classification

Convolutional neural networks

Image fusion

Classification systems

Convolution

Data modeling

Feature extraction

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