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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. 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

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

  2. Objective • Find Clusters • Car images

  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]

  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 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%

  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: 59.36 (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. 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.

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