Compact Descriptors for Visual Search for Money Recognition

Undergraduate thesis
Compact Descriptors for Visual Search
for Money Recognition
Student: Pham Tran Huong Giang
Supervisor: Dr. Le Thanh Ha

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Major: Computer Science – Faculty: Information Technology
University of Engineering and Technology


Outline
 Introduction
 Compact Descriptors for Visual Search
 Approach

 Conclusion and future work

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Introduction
 Compact Descriptors for Visual Search
 International standard for visual search systems
 Formed and developed by MPEG

 Will be used in a great number of visual search

applications

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Introduction
 Money recognition: a practical application for businessman

and tourists. It is hard to recognize local money in the first
use when they come to a foreign country.
 Motivated application: money recognition for smartphone

Study CDVS and evaluate the ability of using CDVS to solve

money recognition problem
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Compact descriptor for visual search
 Extracting compact descriptor
 Retrieval
 Pairwise matching

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Compact Descriptors for Visual Search
 Extracting compact descriptor

Detect key points

Select features

Aggregate global descriptor

Encode feature’s coordinate
Compact
descriptor

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Describe

Compress local
descriptors

Local
descriptors


Compact Descriptors for Visual Search
 Retrieval
Query
image

Extract
compact
descriptor

Compare
global
descriptors

Descriptor
database

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Top
match list


Compact Descriptors for Visual Search
 Pairwise matching
Referent
images

Query
image

Extract
descriptor

Match local
descriptors in
compressed
domain

Check the
geometric
consistency
Matching

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Homography

Extract
descriptor


Approach
 To evaluate:
 Collect a set of images of money as training set
 Build a small program to recognize money

 Test by another set of images (test set)

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Approach
 Collecting training dataset
 Consists of the money of 6 countries: Vietnam, Laos, Cambodia,
Japan, Thailand, Singapore
 Good condition of displayed money
 Without background
 Uniform distributed light
 238 images: 160 images of banknote and 78 images of coin

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Approach
 Building money recognition program

Input
image

Find and
extract
circle

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Extract
descriptor

Extract
descriptor

Match with 160
descriptors of
banknote

Match with 78
descriptors of
coin

Evaluate by
compare with 2
threshold one

of banknote and
one of coin

Result

How much?
Nationality?


Approach
 Testing and discussion
 Test set: 6 subsets
1.

2.
3.

4.
5.
6.

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100 images of banknotes (same type with images in training dataset,
taken by me)
50 downloaded images from the Internet (same type with training
images)
60 images of coins (same type with training images, taken by me)
25 distractor images of banknotes
10 distractor images of coins

20 distractor images without money


Testing result
 For threshold of 100 for banknote and 50 for coin
Subset

Precision

Number of “not found”

Number of false results

results

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1

70/100

30

0

2

3/60

54

3

3

46/50

3

1

4

20/20

20

0

5

18/20

18

2

6

10/10

10

0


Discussion
 Some successful tests in subset 1

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Discussion
 Some “not found” tests in subset 1

 Comment: Money in these images is folded or strong line shine
through the money

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Discussion
 In subset 2: 3 “not found” cases are caused by the low quality

of images

 One “false” case because of the similarity between 2 images:

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Discussion
 CDVS treats well with distractor images (subset 4, 5, 6)
 Only 2 wrong cases in subset 5 because the distractor images are
similar with training images

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Discussion
 For coins, the recognition accuracy is hardly acceptable

because of some reasons
 Coin is poor in feature
 Strongly response to light
 Whole-colorized
 Small surface

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Conclusion and future work
 Conclusion:
 CDVS brings high recognition accuracy for banknote if the image
of banknote is taken in not too bad condition.
 The recognition accuracy for coin is hardly acceptable.

 Future work:
 Collect more data
 Build an mobile application for tourists to recognize all kind of

money in the world.

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Thank you for watching

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