Abstract
In this paper, we propose a clustering method in order to effectively present image search results on the Web. In order to reflect the difference of image semantics among the images, we use the meta-tags added by social tagging. Furthermore, we use low-level image features and photo metadata in order to consider the image looks and photo-taking conditions. We applied constrained agglomerative clustering method with must-link constraints for better clustering results by using multiple similarity metric. We conducted experiments to demonstrate that the proposed method effectively clusters image search results comparing to the traditional weighted similarity aggregation approach, and in some cases the clustering performance of our approach is better than other existing approaches for clustering tasks on an online photo sharing site.
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Hirota, M., Yokoyama, S., Fukuta, N., Ishikawa, H. (2010). Constraint-Based Clustering of Image Search Results Using Photo Metadata and Low-Level Image Features. In: Lee, R. (eds) Computer and Information Science 2010. Studies in Computational Intelligence, vol 317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15405-8_14
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DOI: https://doi.org/10.1007/978-3-642-15405-8_14
Publisher Name: Springer, Berlin, Heidelberg
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