Abstract
Multiple Instance Clustering (MIC) is the problem that cluster objects, each of which is represented by multiple vectors (instances). For solving MIC, two major approaches have been proposed, i.e., between-set-distance based and maximum-margin based. This paper presents another approach, commonality based MIC. In the case of image-set clustering, images preserving strong common local features form a cluster. In this approach, image variations that do not break the common features do not affect the clustering result. We define four commonality measures based on Diverse Density, that are used in agglomerative clustering. Through comparative experiments, we confirmed that two of our methods perform better than other methods examined in the experiments.
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References
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60, 91–110 (2004)
Bay, H., Ess, A., Tiytelaars, T., Gool, L.J.V.: Surf: Speeded up robust features. CVIU 110, 346–359 (2008)
Fei-fei, L.: A bayesian hierarchical model for learning natural scene categories. In: CVPR, pp. 524–531 (2005)
Maron, O., Lozano-Pérez, T.: A framework for multiple-instance learning. In: Advances in Neural Information Processing Systems, pp. 570–576. MIT Press (1998)
Maron, O., Ratan, A.L.: Multiple-instance learning for natural scene classification. In: The Fifteenth International Conference on Machine Learning, pp. 341–349. Morgan Kaufmann (1998)
Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: SODA 2007: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035. Society for Industrial and Applied Mathematics, Philadelphia (2007)
Ward, J.: Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58, 236–244 (1963)
Berkhin, P.: A survey of clustering data mining techniques. Grouping Multidimensional Data, 25–71 (2006)
Forgy, E.: Cluster analysis of multivariate data: Efficiency versus interpretability of classification. Biometrics 21, 768–769 (1965)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Cam, L.M.L., Neyman, J. (eds.) Proc. of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press (1967)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons (1990)
Jain, A., Dubes, R.: Algorithms for Clustering Data. Prentice-Hall (1988)
Zhang, M.L., Zhou, Z.H.: Multi-instance clustering with applications to multi-instance prediction. Appl. Intell. 31, 47–68 (2009)
Zhang, D., Wang, F., Si, L., Li, T.: M3ic: Maximum margin multiple instance clustering. In: Boutilier, C. (ed.) IJCAI, pp. 1339–1344 (2009)
Zhang, D., Wang, F., Si, L., Li, T.: Maximum margin multiple instance clustering with applications to image and text clustering. IEEE Transactions on Neural Networks 22, 739–751 (2011)
Zhang, Q., Goldman, S.A.: Em-dd: An improved multiple-instance learning technique. In: Advances in Neural Information Processing Systems, pp. 1073–1080. MIT Press (2001)
Witten, I.H., Frank, E., Holmes, G.: Data mining: practical machine learning tools and techniques. The Morgan Kaufmann series in data management systems. Morgan Kaufmann, Amsterdam (2011)
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Fukui, T., Wada, T. (2014). Commonality Preserving Image-Set Clustering Based on Diverse Density. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8887. Springer, Cham. https://doi.org/10.1007/978-3-319-14249-4_25
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DOI: https://doi.org/10.1007/978-3-319-14249-4_25
Publisher Name: Springer, Cham
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