![](bg4.png)
50 images from different finger categories are
randomly selected as queries.
Table 2: Retrieval Rate on LEMS Fingerprint database.
Number of top matches
Methods 1 2 4 6 8
GF 100 100 95.36 82.25 39.23
36GFDs 100 91.23 57.23 31.07 22.22
60GFDs 100 91.62 58.01 31.77 22.27
36GFDs+GF 100 100 93.9 77.98 51.12
60GFDs+GF
100 100 96.54 82.72 54.24
It shows from table 2 that 60GFDs+GF achieves
the best retrieval rate. GF gets very high retrieval
rate for fingerprint database. There is little
difference (2%) between GF and GFD+GF in the
retrieval rate, but GFD gives very low retrieval rate.
The results indicate that GF and GFD+GF are much
more effective for fingerprint database than GFD.
3.3 Experiment 3-Object Image
Database
This database is collected by Amsterdam Library of
Object Images (ALOI). ALOI is a color image
collection of one thousand small objects. The images
are systematically varied from viewing angle,
illumination angle, and illumination color for each
object.
Our database consists 1200 images, and it’s
organized into 50 groups while 24 similar images in
each group. In our experiment, we use gray level
image for observation since we only extract shape
and texture features of images. 50 images (one
image per class) are randomly selected as queries.
Table 3: Retrieval Rate on ALOI database.
Number of top matches
Methods 1 4 12 20 24
GF 100 89.09 67.84 55.18 36.05
36GFDs 100 100 73.63 54.3 38.11
60GFDs 100 100 87.6 57.5 42.8
36GFDs+GF 100 100 88.64 76.52 49.77
60GFDs+GF
100 100 89.02 78.10 59.55
It can be seen from Table 3 that 60GFDs+GF
outperforms GFD and GF on ALOI database. Both
of 36GFDs and 60GFDs achieve high retrieval rate,
while GF has the lowest performance. In table 3, it
shows 60GFDs has higher performance (average 5%)
than 36GFDs. Although GF has the lowest retrieval
rate, the overall performance is still good and it
significantly improves the retrieval rate (average
13%) when combine GF and GFD together for
image retrieval. The results indicate that both GFD
and GF are effective for ALOI database and the
combination of GFD and GF achieve the highest
retrieval rate and effectively improve the overall
performance.
4 CONCLUSIONS
In this paper, we investigate and compare shape
feature extraction by GFD and texture feature
extraction by GF on three different databases. From
the experiment results, we come to a conclusion that
GFD+GF can be used as a robust feature of shape
and texture for image retrieval with highest
performance. GFD is effect for shape database and
ALOI database while it’s quite weak for fingerprint
database, since GFD is a shape descriptor. GF gets
very high retrieval rate for fingerprint database, but
gives relatively low performance for shape database
and ALOI database, since GF mainly extracts
texture feature. Scale and translation invariance are
to be considered for GF and the selection of weight
factors is left for future work.
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