CHAPTER 3 PROPOSED system
3.3 Combining Recognition Method
3.3.1 Combining Recognition
In the combining recognition phase, the flower and leaf images of a particular
plant were obtained. Then, the features of the flower and leaf are extracted by the
steps described in sections 3.1 and 3.2. After that, the distances between the query
image and all images in the database are calculated and then the distances are ranked.
The steps are listed below:
1. Get the Top-40 nearest neighbors for the flower and give scores to each rank such
as 1st = 40, 2nd = 39 …, respectively. Next, sum up the scores of the same species.
2. Get the Top-40 nearest neighbors for the leaf and give scores to each rank such as Fig. 21 The flow chart of the proposed method for combining recognition.
Preprocessing Feature Extraction
Combining
Recognition Result Preprocessing Feature
Extraction
1st = 40, 2nd = 39 …, respectively. Next, sum up the scores of the same species.
3. Preserve the species appearing both in step1 and step2. Sum up the scores of the
same species in step1 and step2 as the similarity measure.
4. Rank the similarity measure and return the possible species.
Finally, we can get the most similar species of the query images and the results are
better than using only flower or leaf image.
CHAPTER 4
EXPERIMENTAL RESULTS
In this chapter, experiments are conducted to evaluate the performance of the
proposed method. Firstly, the recognition results of flowers are presented based on
two databases. One is our database of 684 flower images consisting of 24 species and
the other is Zou-Nagy’s [7] database of 612 flower images with 102 species. The
performance of our system in flower recognition will be compared with Zou-Nagy’s
method. Secondly, the recognition results of leaves are presented based on two
databases. One is our database of 960 leaf images consisting of 48 species. The other
is Lee-Chen’s [11] database of 600 leaf images with 60 species. We will compare the
performance between our method and Lee-Chen’s method. Thirdly, the recognition
results of combining system are presented based on two databases. One is our
database consisting of 16 species, including 320 flower images and 320 leaf images.
The other is Saitoh-Kaneko’s [1] database containing 16 species with 320 flower
images and 320 leaf images. The performance of our system will be compared with
Saitoh-Kaneko’s method. Finally, we will compare the performance between our three
recognition methods.
Each flower image in our database is re-scaled to 320x240 pixels. We pick out
one flower image in our database as our query image and consider the remaining 683
flower images as the training data. The results are shown in Table 2. The first row of
Table 2 is measured by returning the Top-5 most similar images to the query image of
the flower recognition method. The second row of Table 2 is measured by returning
the Top-5 most similar species to the query image of the flower recognition method.
Table 2 Performance on our flower database.
Recognition rate (%)
Top-1 Top-2 Top-3 Top-4 Top-5
Number of images
Number of species Similar
images 81.43 89.33 92.25 94.44 96.35 684 24
Similar
species 76.9 93.12 98.25 99.12 99.71 684 24
We also conduct the proposed method on Zou-Nagy’s [7] database collected
from [17]. All images in Zou-Nagy’s database have the same size of 320x240. There
are six images for each species. Some pictures are quite out of focus and the objects
are too small and overlapping. The results are shown in Table 3. We can see that the
processing time of our method is faster than Zou-Nagy’s and the Top-3 recognition
rate (87.6%) is much higher than Zou-Nagy’s (79%) with 8.5 seconds by user’s
rose-curve adjustments before labeling the flower to the class. Although Zou-Nagy’s
method with human help can achieve 93%, it cost much time (10 seconds per flower).
Table 3 Performance comparison between our method and Zou-Nagy’s method using
Zou-Nagy’s database.
Recognition rate (%) Time (s)
Top-1 Top-2 Top-3 Top-4 Top-5 Our method
(similar images) 4.3 76.1 83.8 87.6 90.5 91.3
Our method
(similar species) 4.3 67.3 84.2 92.2 93 93.5
Zou-Nagy’s method
(before labeling) 8.5 52 - 79 - -
Zou-Nagy’s method
(labeling) 10.7 93 - - - -
In our leaf recognition method, each species of leaf includes 40 images; 20 of
them are selected as database images and the remaining are used for testing data. The
results are shown in Table 4 and Table 5. The results of Table 4 are measured by
returning the Top-5 most similar images, and the results of Table 5 are measured by
returning the Top-5 most similar species. The first row of Table 4 and Table 5 is the
performance of the leaves having blooming flowers. The second row of Table 4 and
Table 5 is the performance of the other leaves. The third row of Table 4 and Table 5 is
the performance of all leaves in our database.
Table 4 Performance on our leaf database by returning Top-5 images.
Recognition rate (%)
No. Top-1 Top-2 Top-3 Top-4 Top-5
Number of images
Number of species
1 74.8 86.7 91.5 94 95.8 480 24
2 62.1 76 83.3 87.3 91.3 480 24
3 58.1 71.5 79.9 84.7 87.9 960 48
Table 5 Performance on our leaf database by returning Top-5 species.
Recognition rate (%)
No. Top-1 Top-2 Top-3 Top-4 Top-5
Number of images
Number of species
1. 68.5 90.4 96 98.3 99.4 480 24
2 60 79 88.3 94.6 96.9 480 24
3 52.6 73.1 82.1 89.1 93.2 960 48
We also conduct the proposed method on Lee-Chen’s [4] database. Each image
size of Lee-Chen’s database is 640x480 pixels. Each species in their database includes
15 images; 10 of them are database images and the others are used for testing data.
The results are shown in Table 6. We can see that the recall rate of our method (51.4%)
is much higher than Lee-Chen’s (48.2%) from Table 6. However, Lee and Chen tuned
the weights of features to achieve the optimal recognition rate. Hence, our recognition
rate (70%) is lower than Lee-Chen’s (82.33%) by returning the most similar image.
Nevertheless, our recognition rate can achieve 94.33% by returning the Top-5 most
similar species and it is higher than Lee-Chen’s.
Table 6 Performance comparison between our method and Lee-Chen’s method using
Lee-Chen’s database.
Recognition rate (%)
Top-1 Top-2 Top-3 Top-4 Top-5
Recall rate (%) Our method
(similar images) 70 77.67 84.33 88.67 91.67 51.4
Our method
(similar species) 65 82 88.33 93 94.33 51.4
Lee-Chen’s method 82.33 - - - - 48.2
In order to compare the performance with Saitoh-Kaneko’s [1] method, we
collected the same numbers of species and images with Saitoh-Kaneko’s database, the
reason is that we can not get their database. The recognition results are listed in Table
7. We can see that the recognition rate of our method is much higher than
Saitoh-Kaneko’s method.
Table 7 Performance comparison between our method and Saitoh-Kaneko’s method
using the same numbers of Saitoh-Kaneko’s database.
Recognition rate (%)
Top-1 Top-2 Top-3
Our method 97.5 100 100
Saitoh-Kaneko’s method 96.03 99.26 99.26
Finally, we compare the performance of our recognition methods: flower, leaf
Only flower images of 24 species; (ii) Only leaf images of 24 species which
correspond to (i); (iii) A pair of flower and leaf images of 24 species. Table 8 shows
the performance results. From Table 8, we can see that the combining method get
higher recognition rate than those using only flower or leaf image. Hence, the
combining recognition method is more effective and can provide better results to user.
Table 8 Performance comparison.
Recognition rate (%) Method
Top-1 Top-2 Top-3 Top-4 Top-5
Number of images
Number of species Our method
(Flower) 76.9 93.1 98.3 99.1 99.7 684 24
Our method
(Leaf) 68.5 90.4 96 98.3 99.4 480 24
Our method
(Combining) 94.4 99.7 100 100 100 684 24
We have built a plant recognition system written in Java language on a PC. Figs.
22(a), 22(b) and 22(c) are the interfaces for the flower, leaf and combining
recognition systems, respectively. Figs. 23(a), 23(b) and 23(d) are the interface for
recognition results of flower, leaf and combining recognition systems respectively.
After we retrieved the candidate images, users can click on the image to get the
system to know the species of the plant which they did not know before.
(a) (b)
(c)
Fig. 22 Interfaces of recognition system. (a) Flower recognition system. (b) Leaf recognition system. (c) Combining recognition system.
(a)
(b)
Fig. 23 Interfaces for recognition results. (a) Flower recognition results. (b) Leaf
(c)
(d)
Fig. 23 Interfaces for recognition results. (a) Flower recognition results. (b) Leaf recognition results. (c) The retrieved information of the query image. (d) Combining recognition results.
CHAPTER 5 CONCLUSION
In this thesis, we have proposed a plant recognition system based on leaf and
flower. In the flower recognition system, we use an automatic segmentation based on
human visual system. Then, a simple and interactive user interface is applied.
According to the shape and color features of the flower, 14 features are extracted from
the segmented flower image. Finally, a similarity measure is provided to do
recognition.
In the leaf recognition system, we also proposed an automatic segmentation
method and a solution to treat rotation problem. Next, we extract 5 features according
to the characteristics of the leaf shapes. Then, we preserve possible species and find
out the similar images from leaf image database by similarity distance.
In the combining recognition system, a new method has been proposed for
recognizing plants based on leaf and flower. Firstly, the features of the leaf and flower
are extracted. Next, we calculate the similarity between the query image and database
images of leaf and flower and then combine the results of leaf and flower. Finally, the
system can find out the most similar species.
recognition system. This means that our combining recognition system can get higher
accuracy rate than the single recognition system.
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