Finding Clusters within a Class to Improve Classification Accuracy

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Presentation transcript:

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

Objective Car images Find Clusters

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

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 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: 81.86% Mean accuracy: 82.87%

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 Baseline (no-clusters): mean accuracy: 57.42 (1.13) % Clusters (k=3) mean accuracy: 59.36 (1.05) %

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

Questions

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.