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CIS595-Presentation. Clustering Analysis of the Object Shape Data Based on Time Sequence. Presented by: Wang , Qiang. Agenda. Introduction Proposed techniques Experimental results Conclusion. Introduction. Shape description Time sequence

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cis595 presentation
CIS595-Presentation

Clustering Analysis of the Object Shape Data Based on Time Sequence

Presented by: Wang , Qiang

agenda
Agenda
  • Introduction
  • Proposed techniques
  • Experimental results
  • Conclusion
introduction
Introduction
  • Shape description
  • Time sequence
  • Data set: 1400 images ( 70 classes * 20 objects)
slide4

Proposed Technique

Clustering Algorithms:

  • K-means clustering
  • Agglomerative Clustering
slide5

Proposed Technique

Multidimensional Scaling

n n

STRESS(X) =  wij(ij-dij)2

i=1 j=1

ij : the reproduced distance ( in new space)

dij: the original distance

Wij: Weight (to keep Stress falls in [0,1])

slide6

Randomly choose the original cluster centers

Choose original centers with prior knowledge

Over the whole data set(1400 objects)

0.2871

0.3750

Over the subset with good quality(300 objects)

0.9400

1.00

Experimental results

K-means Clustering

Definition of Correctness:

if above 60% objects from a class are clustered together, we

say this class is a correctly clustered.

Result:

slide7

Experimental results

Agglomerative Clustering

Single: Distance(:, MinI) = min(Distance(:, MinI), Distance(:, MinJ))

Complete:Distance(:, MinI) = max(Distance(:, MinI), Distance(:, MinJ));

Centroid: Distance(:, MinI) = (Distance(:, MinI) + Distance(:, MinJ))/2;

‘Complete’ algorithm has the best experimental result!

slide8

Experimental results

Agglomerative Clustering

Over the whole data set(70 clusters):

[1x88 ] [1x130 ] [1x49 ] [1x16 ] [1x75 ] [1x36 ] [1x37 ] [1x30 ] [1x41 ] [1x22 ]

[1x10 ] [1x7 ] [1x10 ] [1x16 ] [1x115 ] [1x39 ] [1x12 ] [1x34 ] [1x24 ] [1x7 ]

[1x4 ] [1x17 ] [1x95 ] [1x16 ] [1x6 ] [1x6 ] [1x8 ] [1x32 ] [1x14 ] [1x46 ]

[1x35 ] [1x29 ] [1x19 ] [1x5 ] [1x15 ] [1x15 ] [1x27 ] [1x5 ] [1x22 ] [1x14 ]

[1x4 ] [1x4 ] [1x10 ] [1x4 ] [1x18 ] [1x21 ] [1x4 ] [1x4 ] [1x4 ] [1x4 ]

[1x4 ] [1x2 ] [1x3 ] [1x6 ] [1x4 ] [1x3 ] [1x16 ] [1x2 ] [1x5 ] [1x6 ]

[1x6 ] [1x7 ] [1x2 ] [1x2 ] [1x4 ] [1x8 ] [1x8 ] [1x4 ] [1x2 ] [1334]

slide9

Experimental results

Agglomerative Clustering

Over subset(15 clusters) with good quality:

[1x22 ] [1x20 ] [1x22 ] [1x20 ] [1x21 ]

[1x36 ] [1x20 ] [1x21 ] [1x34 ] [1x23 ]

[1x16 ] [1x12 ] [1x8 ] [1x4 ] [1x21 ]

slide10

Experimental results

MDS Visualization: (randomly chosen 7 classess)

slide11

Experimental results

MDS Visualization: (7 classes with good quality)

slide12

Experimental results

MDS Visualization: (random original configuration )

slide13

Conclusion

It’s feasible to transform image contour data to time sequence;

Prior knowledge will improve the clustering results;

The quality of data set decide the analysis result;

MDS is a good method to visualize the clustering result.