=Paper=
{{Paper
|id=Vol-1391/82-CR
|storemode=property
|title=Plant Identification with Deep Convolutional Neural Network: SNUMedinfo at LifeCLEF Plant Identification Task 2015
|pdfUrl=https://ceur-ws.org/Vol-1391/82-CR.pdf
|volume=Vol-1391
|dblpUrl=https://dblp.org/rec/conf/clef/Choi15a
}}
==Plant Identification with Deep Convolutional Neural Network: SNUMedinfo at LifeCLEF Plant Identification Task 2015==
https://ceur-ws.org/Vol-1391/82-CR.pdf
Plant identification with deep convolutional neural
network: SNUMedinfo at LifeCLEF plant identification
task 2015
Sungbin Choi
Department of Biomedical Engineering, Seoul National University, Republic of Korea
wakeup06@empas.com
Abstract. This paper describes our participation at the LifeCLEF Plant identifi-
cation task 2015. Given various images of plant parts such as leaf, flower or stem,
this task is about identification of plant species given multi-image observation
query. We utilized GoogLeNet for individual image classification, and combined
image classification results for plant identification per observation. Our approach
achieved best performance in this task.
Keywords: Image classification, Deep convolutional neural network, Goog-
LeNet, Borda-fuse
1 Introduction
In this paper, we describe the participation of the SNUMedinfo team at the LifeCLEF
Plant Identification task 2015. Each query is composed of multi-image observation,
which represents individual plant observed the same day by a same person. Each ob-
servation has multiple image, taken from various parts of plant such as leaf, stem or
flower. So this task is about identification of plant species given multi-image observa-
tion query. For a detailed introduction of the task, please see the overview paper of this
task (1).
In recent years, deep Convolutional Neural Network (CNN) has improved automatic
image classification performance dramatically (2). In this study, we experimented with
GoogLeNet (3) which has shown effective performance in recent ImageNet Challenge
(4). Although LifeCLEF Plant identification task is about more fine-grained image clas-
sification compared to ImageNet’s general object category classification, finetuning
CNN pretrained on ImageNet dataset was very effective in performance. Our experi-
mental methods are detailed in the next section.
2 Methods
We applied CNN for individual image classification (Section 2.1). Then image classi-
fication results are combined to produce observation classification (Section 2.2).
2.1 Image classification using deep convolutional neural network
Finetuning from GoogLeNet
We utilized GoogLeNet for individual plant image classification. GoogLeNet incor-
porates Inception module with the intention of increasing network depth with compu-
tational efficiency.
We randomly divided observations in LifeCLEF Plant identification training set into
five-fold. Images from one fold is used as validation set, and images from other four
fold is used as training set.
Training CNN for plant identification started from GoogLeNet pretrained on
ImageNet dataset. We finetuned CNN on plant identification training set (initial learn-
ing rate 0.001; batch_size:120; number of iteration:100,000). Only horizontal mirror-
ing (left-right flipping of image) and image random cropping (cropping 224 x 224 im-
age out of 256 x 256 input image) is used for data augmentation.
We trained five separate CNNs1. CNN output score is used to produce ranked list of
relevant plant species. Five ranked list is combined into single ranking using Borda-
fuse method (5).
2.2 Observation classification by combining image classification result
Each query observation is composed of multiple image. We combined image classifi-
cation result from Section 2.1 using two different rank aggregation method.
(1) Borda-fuse method
(2) Majority voting based method
3 Results
We submitted four different runs. Details of runs are summarized in the following
table.
Table 1. Different setting of submitted runs
Image classification Observation classification
SNUMedinfo1 Only 1 CNN is used2 Borda-fuse
SNUMedinfo2 Only 1 CNN is used Majority voting based method
SNUMedinfo3 5 CNNs are used Borda-fuse
SNUMedinfo4 5 CNNs are used Majority voting based method
1 We arbitrarily determined number of CNN classifier for experiment as five. In this study, we
tried to assess the effects on performance when more CNNs are trained and their results are
combined.
2 Among five trained CNNs, only one CNN is used for classification.
Primary evaluation metric for this task was average classification score. Inverse of the
rank of the correct species are scored between 0 and 1, and then it is macro-averaged
over distinct user who has taken photos of observation query images.
Evaluation results on test set is described in following table.
Table 2. Evaluation results of submitted runs
Image classification Observation classification
score score
SNUMedinfo1 0.594 0.604
SNUMedinfo2 0.594 0.611
SNUMedinfo3 0.652 0.663
SNUMedinfo4 0.652 0.667
Performance was clearly better when five CNNs are combined for image classifica-
tion (SNUMedinfo3 and SNUMedinfo4), compared to when only one CNN is used
(SNUMedinfo1 and SNUMedinfo2). This is observed from both per image classifica-
tion score and per observation classification score.
With regard to the rank aggregation methods used in observation classification, ma-
jority-voting based method showed slightly better performance compared to the
Borda-fuse method, but the difference was negligible.
4 Discussion
4.1 CNN finetuning from other task model
In Chen et al.’s experiments (6) in last year, CNN trained without finetuning from
other external dataset showed inferior performance, compared to their advanced feature
encoding method (7) based on SIFT and Color Moments features. But when CNN is
finetuned from ImageNet pretrained GoogLeNet, it was very effective, even though
plant identification is targeted for finer-grained image classification task between dif-
ferent plant species compared to the ImageNet’s general object category classification.
4.2 Combining CNN output
From table 2, we could observe that training multiple CNN and combining their out-
puts improve classification performance. As also experimented in (8), training and
combining multiple CNN output method is considered to be effective to cope with
CNN’s variance.
4.3 Training plant part-specific CNN
In this task, each image is tagged with plant part name (e.g., stem, flower). We also
tried dividing training set images according to the tagged part and training CNN per
each part separately. But in our preliminary experiments, these part-specific image
trained CNNs mostly showed no performance gain (similar or slightly worse perfor-
mance, compared to when no part-specific training is used). So we chose not to use
tagged plant part information for CNN training.
5 Conclusion
In LifeCLEF Plant identification task 2015’, we applied GoogLeNet pretrained on
ImageNet dataset for training by finetuning on the plant training set. Although task is
more finer-grained image category classification compared to the ImageNet, and the
number of plant species has doubled compared to the last year’s plant task (9), classi-
fication performance was very effective. Also, training multiple CNNs and combining
their output improved classification performance further. In our future study, we will
explore other CNN architectural design options and different classification result com-
bination methodologies.
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