Score-based Fusion Schemes for Plant Identification from Multi-organ Images
This paper describes some fusion techniques for achieving high accuracy species identification from images of different plant organs. Given a series of different image organs such as branch, entire, flower, or leaf, we firstly extract confidence scores for each single organ using a deep convolutiona...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
H. : ĐHQGHN
2019
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| Subjects: | |
| Online Access: | http://repository.vnu.edu.vn/handle/VNU_123/64759 https://doi.org/10.25073/2588-1086/vnucsce.201 |
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| Institution: | Vietnam National University, Hanoi |
| Language: | English |
| Summary: | This paper describes some fusion techniques for achieving high accuracy species identification from images of different plant organs. Given a series of different image organs such as branch, entire, flower, or leaf, we firstly extract confidence scores for each single organ using a deep convolutional neural network. Then, various late fusion approaches including conventional transformation-based approaches (sum rule, max rule, product rule), a classification-based approach (support vector machine), and our proposed hybrid fusion model are deployed to determine the identity of the plant of interest. For single organ identification, two schemes are proposed. The first scheme uses one Convolutional neural network (CNN) for each organ while the second one trains one CNN for all organs. Two famous CNNs (AlexNet and Resnet) are chosen in this paper. We evaluate the performances of the proposed method in a large number of images of 50 species which are collected from two primary resources: PlantCLEF 2015 dataset and Internet resources. The experiment exhibits t he dominant results of the fusion techniques compared with those of individual organs. At rank -1, the highest species identification accuracy of a single organ is 75.6% for flower images, whereas by applying fusion technique for leaf and flower, the accuracy reaches to 92.6%. We also compare the fusion strategies with the multi -column deep convolutional neural networks (MCDCNN) [1]. The proposed hybrid fusion scheme outperforms MCDCNN in all combinations. It obtains from + 3.0% to + 13.8% improvement in rank-1 over MCDCNN method. The evaluation datasets as well as the source codes are publicly available |
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