1. Finding Clusters within a Class to Improve Classification Accuracy
Final Project
Yong Jae Lee
4/28/08

2. Objective
Car images
Find Clusters

3. Approach Object Representation: Scale Invariant Feature Transform
(SIFT) [Lowe. 2004]
Image to Image Similarity:
Proximity Distribution Kernels [Ling et al. 2007]
Clustering:
Normalized Cuts [Shi et al. 2001]
Classification:
Support Vector Machines [Vapnik et al. 1995] X1 X2 X3 X4
K11
K12
K13
K14
K21
K22
K23
K24
K31
K32
K33
K34
K41
K42
K43
K44

4. Dataset 1 PASCAL VOC 2005 4 categories:
motorbikes, bicycles, people, cars
Train set:
[214, 114, 84, 272] (684)
Test set:
[216, 114, 84, 275] (689)

5. Results 1 Baseline (no-clusters) Clusters (k=3) m 94.9 5.1 12.3 71.9
5.1
12.3
71.9
5.26
10.5
10.7
11.9
32.1
45.2
2.9
2.6
3.6
90.9 m
95.4
4.6
13.2
73.7
3.5
9.7
10.7
11.9
34.5
45.2
2.2
4.0
91.6 b b
true labels p p c c m b p c m b p c
predicted labels
Mean accuracy: %
Mean accuracy: %

6. Dataset 2 Caltech-101 101 object categories
9097 images (30-80 per class)
30 images / class
15 train, 15 test
10 runs cross-validation

7. Results 2 Baseline (no-clusters): mean accuracy: 57.42 (1.13) %
Clusters (k=3)
mean accuracy: (1.05) %

8. Future work Automatically determine k
- analyze eigenvalues of the Laplacian of affinity matrix [Ng et al. 2001]
- significant difference between two consecutive eigenvalues determines how many clusters there are
Comparison with other classifiers
- e.g., k-Nearest Neighbor: labels are determined by majority labels of train instances to the test instance

9. Questions

10. References
H. Ling and S. Soatto, “Proximity Distribution Kernels for Geometric Context in Category Recognition,“ IEEE 11th International Conference on Computer Vision, pp. 1-8, 2007.
D. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints," International Journal of Computer Vision, vol. 60, no. 2, pp , 2004.
J. Shi and J. Malik, “Normalized cuts and image segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp , 2000.
C. Cortes and V. Vapnik, “Support-vector networks," Machine Learning, vol. 20, no. 3, pp , 1995.
M. Everingham, A. Zisserman, C. K. I. Williams, L. Van Gool, et al. “The 2005 PASCAL Visual Object Classes Challenge,” In Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Textual Entailment., eds. J. Quinonero-Candela, I. Dagan, B. Magnini, and F. d'Alche-Buc, LNAI 3944, pages , Springer-Verlag, 2006.
A. Ng, M. Jordan and Y. Weiss. “On spectral clustering: Analysis and an algorithm” In Advances in Neural Information Processing Systems 14, 2001
L. Fei-Fei, R. Fergus, and P. Perona. “Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories”. In Proceedings of the Workshop on Generative-Model Based Vision. Washington, DC, June 2004.