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Finding Clusters within a Class to Improve Classification Accuracy. Final Project Yong Jae Lee 4/28/08. Objective. Find Clusters. Car images. Approach. Object Representation: Scale Invariant Feature Transform (SIFT) [Lowe. 2004] Image to Image Similarity:

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finding clusters within a class to improve classification accuracy

Finding Clusters within a Class to Improve Classification Accuracy

Final Project

Yong Jae Lee

4/28/08

objective
Objective
  • Find Clusters
  • Car images
approach
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]

dataset 1
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)

results 1
Results 1
  • Baseline (no-clusters)
  • Clusters (k=3)

m

m

b

b

true labels

p

p

c

c

m

b

m

b

p

c

p

c

predicted labels

Mean accuracy: 81.86%

Mean accuracy: 82.87%

dataset 2
Dataset 2
  • Caltech-101
  • 101 object categories

9097 images (30-80 per class)

  • 30 images / class
  • 15 train, 15 test
  • 10 runs cross-validation
results 2
Results 2
  • Baseline (no-clusters):

mean accuracy: 57.42 (1.13) %

  • Clusters (k=3)

mean accuracy: 59.36 (1.05) %

future work
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

references
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. 91-110, 2004.
  • J. Shi and J. Malik, “Normalized cuts and image segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888-905, 2000.
  • C. Cortes and V. Vapnik, “Support-vector networks," Machine Learning, vol. 20, no. 3, pp. 273-297, 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 117-176, 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.