A Parallel Convolution and Decision Fusion-Based Flower Classification Method

Lianyin Jia, Hongsong Zhai, Xiaohui Yuan, Ying Jiang and Jiaman Ding
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Lianyin Jia: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
Hongsong Zhai: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
Xiaohui Yuan: College of Engineering, University of North Texas, Denton, TX 76203, USA
Ying Jiang: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
Jiaman Ding: Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China

Mathematics, 2022, vol. 10, issue 15, 1-15

Abstract: Flower classification is of great significance to the fields of plants, food, and medicine. However, due to the inherent inter-class similarity and intra-class differences of flowers, it is a difficult task to accurately classify them. To this end, this paper proposes a novel flower classification method that combines enhanced VGG16 (E-VGG16) with decision fusion. Firstly, facing the shortcomings of the VGG16, an enhanced E-VGG16 is proposed. E-VGG16 introduces a parallel convolution block designed in this paper on VGG16 combined with several other optimizations to improve the quality of extracted features. Secondly, considering the limited decision-making ability of a single E-VGG16 variant, parallel convolutional blocks are embedded in different positions of E-VGG16 to obtain multiple E-VGG16 variants. By introducing information entropy to fuse multiple E-VGG16 variants for decision-making, the classification accuracy is further improved. The experimental results on the Oxford Flower102 and Oxford Flower17 public datasets show that the classification accuracy of our method reaches 97.69% and 98.38%, respectively, which significantly outperforms the state-of-the-art methods.

Keywords: image classification; decision fusion; information entropy; parallel convolutions; convolutional neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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