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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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
Print ISBN: 978-3-319-14248-7
Online ISBN: 978-3-319-14249-4
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