A Flower Classification Framework Based on Ensemble of CNNs

Huang, Buzhen; Hu, Youpeng; Sun, Yaoqi; Hao, Xinhong; Yan, Chenggang

doi:10.1007/978-3-030-00764-5_22

Cited by 5 publications

(1 citation statement)

References 23 publications

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“…Experimental results demonstrated that the proposed algorithm achieved superior performance, compared with other data fusion methods. Huang et al [21] presented a flower classification framework based on ensemble CNNs and demonstrated its effectiveness on the Flowers102 dataset. In the author team's previous work [22] , a CNN ensemble method was developed for flower classification.…”

Section: Introduction mentioning

confidence: 99%

Dynamic ensemble selection of convolutional neural networks and its application in flower classification

Wang

et al. 2022

International Journal of Agricultural and Biological Engineering

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In recent years, convolutional neural networks (CNNs) have achieved great success in image classification. However, CNN models usually have complex network structures that tend to cause some related problems, such as redundancy of network parameters, low training efficiency, overfitting, and weak generalization ability. To solve these problems and improve the accuracy of flower classification, the advantages of CNNs were combined with those of ensemble learning and a method was developed for the dynamic ensemble selection of CNNs. First, MobileNet models pre-trained on a public dataset were transferred to flower datasets to train thirteen different MobileNet classifiers, and a resampling strategy was used to enhance the diversity of individual models. Second, the thirteen classifiers were sorted by a classifier sorting algorithm, before ensemble selection, to avoid an exhaustive search. Finally, with the credibility of recognition results, a classifier subset was dynamically selected and integrated to identify the flower species from their images. To verify the effectiveness, the proposed method was used to classify the images of five flower species. The accuracy of the proposed method was 95.50%, an improvement of 1.62%, 3.94%, 22.04%, 13.77%, and 0.44%, over those of MobileNet, Inception-v1, ResNet-50, Inception-ResNet-v2, and the linear ensemble method, respectively. In addition, the performance of the proposed method was compared with five other methods for flower classification. The experimental results demonstrated the accuracy and robustness of the proposed method.

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Section: Introduction mentioning

confidence: 99%