Finding Clusters within a Class to Improve Classification Accuracy
This project explores the identification of clusters within image data to boost classification performance. By utilizing Scale-Invariant Feature Transform (SIFT) for object representation and Proximity Distribution Kernels for image similarity, we apply Normalized Cuts for clustering and Support Vector Machines (SVM) for classification. Testing on datasets like PASCAL VOC 2005 and Caltech-101 reveals improvements in mean accuracy when clusters are introduced. Future work aims to automate cluster determination through eigenvalue analysis and compare with other classifiers like k-Nearest Neighbors.
Finding Clusters within a Class to Improve Classification Accuracy
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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: Proximity Distribution Kernels [Ling et al. 2007] • Clustering: Normalized Cuts [Shi et al. 2001] • Classification: Support Vector Machines [Vapnik et al. 1995]
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 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 • 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
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.
