shape recognition and retrieval based on edit distance and dynamic programming

Shape Recognition and Retrieval Based on Edit Distance and

Dynamic Programming

PAN Hongfei ( 潘鸿飞 ), LIANG Dong ( 梁栋 ), TANG Jun ( 唐俊 ), WANG Nian ( 王年 ), LI Wei ( 李薇 )

Presented byKaidul Islam

0907016

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Glimpse of this Paper

A shape recognition and retrieval algorithm How the algorithm approaches:- Extracting skeletal features(Medial axis

transform)- Transforming features into string of symbols- Similarity measurement(Edit Distance)- Shape identification(Dynamic programming) Analyzing public datasets for comparison with

other approaches.

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Structure Shape Representation

- Shape skeleton - Skeleton feature representation

Computation of Similarity between Strings Shape matching Dynamic Programming Test and Analysis

- Matching Analysis - Comparisons

Future Work3

Shape Skeleton(1/2)

• Skeleton feature satisfy distance and curvature criteria– every point on the skeleton should be

equidistant from two different boundary point– distance between the skeleton point and the

boundary point should be less than the maximum distance between the center of gravity of the contour and the boundary points.

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Shape Skeleton(2/2)

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Future Work6

Transformation from a skeleton to strings

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Future Work8

Edit Distance!

• Edit distance is a way of quantifying how dissimilar two strings are.

• Determine minimum number of operations to turn one string to another.

• Operation include –

- insertion

- Deletion

- Substitution9

Edit Distance(2/2)

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Future Work11

The tabulation , D(i, j) Seq2(j) A R T S

Seq1(i) 0 1 2 3 4

0

M 1

A 2

T 3

H 4

S 512


Seq1(i) 0 1 2 3 4

0 0

M 1

A 2

T 3

H 4

S 513


Seq1(i) 0 1 2 3 4

0 0 1

M 1

A 2

T 3

H 4

S 514


Seq1(i) 0 1 2 3 4

0 0 1 2

M 1

A 2

T 3

H 4

S 515


Seq1(i) 0 1 2 3 4

0 0 1 2 3 4

M 1 1

A 2 2

T 3 3

H 4 4

S 5 516


Seq1(i) 0 1 2 3 4

0 0 1 2 3 4

M 1 1 1

A 2 2

T 3 3

H 4 4

S 5 517


Seq1(i) 0 1 2 3 4

0 0 1 2 3 4

M 1 1 1 2

A 2 2

T 3 3

H 4 4

S 5 518


Seq1(i) 0 1 2 3 4

0 0 1 2 3 4

M 1 1 1 2 3 4

A 2 2 1 2 3 4

T 3 3

H 4 4

S 5 519


Seq1(i) 0 1 2 3 4

0 0 1 2 3 4

M 1 1 1 2 3 4

A 2 2 1 2 3 4

T 3 3 2 2 2 3

H 4 4

S 5 520


Seq1(i) 0 1 2 3 4

0 0 1 2 3 4

M 1 1 1 2 3 4

A 2 2 1 2 3 4

T 3 3 2 2 2 3

H 4 4 3 3 3 3

S 5 5 4 4 4 321

The traceback Seq2(j) A R T S

Seq1(i) 0 1 2 3 4

0 0 1 2 3 4

M 1 1 1 2 3 4

A 2 2 1 2 3 4

T 3 3 2 2 2 3

H 4 4 3 3 3 3

S 5 5 4 4 4 322

Similarity Cost by Dynamic Programming

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Future Work24

Matching Analysis(1/2)Shape sets from the MPEG-7 Shape Silhouette

Database

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Matching Analysis(2/2)Comparison of results for various values of k

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Future Work27

Comparisons(1/3)

Sample shape sets from the MPEG-7 Shape Silhouette Database(1)

Sample shape sets from Plant Species Database(2)

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Comparisons(2/3)

Retrieval rates for Silhouette Database(1)

Retrieval rates for Silhouette Database(2)

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Comparisons(3/3)

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Comparisons(3/3)

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Future Work32

Future Work

• Improve the algorithm

• Introducing other features –

- Texture

- Movement

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shape recognition and retrieval based on edit distance and dynamic programming

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