Shape Matching and
Classification Using Height
Functions
Xide Xia
ENGN 2560
Advisor: Prof. Kimia
Project Midterm Presentation
Schedule
• Week1~2: Debug and Run the codes in my
computer successfully
• Week3~4: Read codes carefully and try to
understand how does each part works and the
relations among them
• Week5~6: Test with different dataset (such as
MPEG-7 data set, Kimia’s data set, ETH-80 data
set)
• Week7:Compare with other shape matching
algorithm (with shock graph)
• Week8: Make conclusion and prepare for the
final presentation
Main.m
• batch_HF .m :
calculating the original height functions features for all shapes in some
data set, and storing them in one .mat file
• hisHF.m :
smoothing and local normalization
• HF_shape_retrieval .m :
DP matching based on height functions
• HF_SC .m :
improving shape similarity values by shape complexity
batch_HF.m
- Compute height function features for images
• sample every image and get scale values
• Contour extraction:
Cs = extract_longest_cont(im, n_contour);
• HF for all landmark points
hf = compu_contour_HF(Cs);
• save for all shapes
hisHF .m :
% smoothing and local normalization
• Smoothed height values:
F is an M *N matrix with column i being the shape descriptor Fi of the
sample point Xi.
HF_shape_retrieval .m :
% DP matching based on height functions
• matching score save: Score = zeros(m-2);
• matching the two shapes with their feature
by DP
• compute the cost matrix b/w points in
feature1 and feature2.
(in current order/ in reverse order)
• get the best result
HF_SC .m :
% improving shape similarity values by shape
complexity
• shape complexity
• add shape complexity to score
Shape descriptor with height functions:
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A sequence of equidistant sample points X
Tangent line Li
Height value Hi
Smoothed height values
Local nomalization
Similarity measure using the height descriptor:
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•
•
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The cost (distance) of matching p and q
Weight coefficient
Dissimilarity between the two shapes
Shape complexity
Dissimilarity normalized by complexity values
compute matching accuracy
bull’s eyes score:
which counts how many objects within the
40 most similar objects belong to the class
of the query object. Every shape in the data
set is used as a query, and the retrieval
result for the whole data set is obtained by
averaging among all shapes.
Test codes with Kimia Dataset
matching accuracy
1. bird
2.bones
3. brick
4.camel
5.car
TODO:
• Test with more different dataset (such as
MPEG-7 data set,ETH-80 data set)
• Compare with other shape matching
algorithm (with shock graph)
• Make conclusion and prepare for the final
presentation
Reference:
• Shape matching and classification using
height functions(Junwei Wanga, Xiang Bai a, Xinge
You, Wenyu Liu, Longin Jan Latecki)