Implementation of Hierarchy Color Image Segmentation for Content-Based Image Retrieval System - Nanik Suciati Shanti Dewi
ISSN 1858-1633 2005 ICTS 83
NoCitra GambarCitra CitraSegm ReprTree UrutanRegion 1
2 3
4 NoCit
ra NoRe
g MR MG MB SR SG SB Densit
y Thinness Invar
1 1 1 2
1 3 2 1
Figure 6. The structure of tables
4. EXPERIMENTAL RESULT
Experiment Data
Image database which is used for experiment is 150 color image 24-b. These images are taken by
Samsung Digimax 301 digital camera with ‘fine’ quality. The experimental image dimension is 640
pixels in width and 480 pixels in height. Theshold Value
Threshold value is shown in table 1.
Table 1. Threshold Value
Threshold Value
T
L
20 T
G
50 T
S
20 T
N
4000
Segmentation Result
Color image segmentation result and relationship tree structure is shown in figure 7. The number labeled
in each node of the relationship tree represent the labels which are assigned during segmentation. Based
on the relationship tree below, it can be seen that the regions are sorted by region size in descending order.
a b
c
Figure 7. Segmentation result and relationship tree a Original image, b Segmentation result, c Relationship tree
Experiment system
Software experiment of image retrieval is done by querying 10 images. For each query object, the images
containing the same object are determined by human eye are defined as ground truth.
After lots of ground truth decided, the experiment is continued to obtain a list of similar images. The
length of this list can be determined by users. In this experiment, the length L are 5, 10, 15 and 20.
Root
4 1
2 6
5 3
String Representasi : nnnnn
Information and Communication Technology Seminar, Vol. 1 No. 1, August 2005
ISSN 1858-1633 2005 ICTS 84
Four query object used in experiment are shown in figure 8.
Query object 1 Query
object 2
Query object 3 Query object 4
Figure 8. Query object 1 to 4
The image retrieval efficiency,
L
η
[6] for length L is shown in the following equation :
⎪ ⎪
⎩ ⎪
⎪ ⎨
⎧ =
L N
N N
L
S T
S
η
13
where N
S
is the number of the similar images retrieved in the result list, and N
T
is the number of the ground truth for query object
Output image of retrieval process for object query 1 to 4 is shown in table 2. Based on table 2, query
object 1 obtains output images 1, 5, 2, 9, 4 and so on. Symbol shows the similar images with query object.
Retrieval efficiency
L
η
for retrieval of 10 query object are shown in table 3. Retrieval efficiency
L
η
for each query object are calculated for length L 5, 10, 15 and 20. Based on table 2, for query object 1
obtained similar images with query object NS for length 5, 5 images, length 10, 6 images, length 15, 7
images, and length 20, 8 images. After the number of ground truth for each query are determined, efficiency
rate can be calculated by equation 13. In table 3, can be seen for query object 1, the retrieval efficiency for
length 5, 10, 15 dan 20 are 1, 0.6, 0.7 and 0.8, respectively.
Table 2. Output images for query 1 to 4
No Query1 Query2 Query3 Query4 1
1 11 21 32 2
5 13 24 38 3
2 17 27 39 4
9 15 26 40 5
4 12 22 37 6
66 16 28 35 7
70 45 30 33
8 78 49 29
19 9
8 47 50 127 10
61 43 18 128 11
64 44 42 103 12 111
48 16
125 13 17
20 19
106 14
71 41 44 36 15
10 100 20 84 16 117
46 12
34 17 7
18 15
10 18
68 42 13 31 19
13 50 43 89 20 18
14 45
90
Table 3. Retrieval efficiency for 10 query object
Citra N
T
L=5 L=10 L=15 L=20 1
10 1 0.6 0.7 0.8
2
10 1 0.6 0.7 0.9
3
10 1 0.8 0.8 0.8
4
10 1 0.7 0.8 1
5
10 1 0.9 1 1
6
10 0.4 0.3 0.4 0.5
7
10 0.6 0.6 0.8 0.9
8
10 1 0.9 0.9 0.9
9
10 0.4 0.3 0.6 0.7
10
10 0.6 0.3 0.4 0.5
Rata- rata
0.8 0.6 0.71 0.8
Retrieval result for query object 2 from rank 1 to 4 are shown in figure 9. And for query object 7 are
shown in figure 10.
11 13
17 15
Figure 9. Rank 1 to 4 for query object 2
If N
T
≤ L if N
T
L
Implementation of Hierarchy Color Image Segmentation for Content-Based Image Retrieval System - Nanik Suciati Shanti Dewi
ISSN 1858-1633 2005 ICTS 85
72 74
89 80
Figure 10. Rank 1 to 4 for query object 7
Efficiency rate for object query 10 is not high. This is because of the query string is simple one-node
region. So, this query string will match with all images in database. So, the decision of output images only
depend on dissimilarity score between query object and all matching images. The retrieval process will
produce wrong output especially when color and shape features of image in database almost similar
with feature of query object, although both images are not similar at all.
Efficiency rate for object query 3 is high enough. This is because of segmentation result which is
showed by relationship tree of query object is not one- node region. So, the query string is more specific.
The mistake of retrieval process is caused by the segmentation produces different relationship tree
structure. The factors which is affected the segmentation process are light and reflection. Object
size in the image also influences segmentation process. If the object size bigger or smaller than query
object, the segmentation process will be different.
The mistake of output image query object 3 can be caused by segmentation. Segmentation of some group
truth are not the same as query string. Therefore, some of ground truth can not be processed.
5. CONCLUSION
