Abstract
Automatically organizing and searching images by their content in large image datasets are major goals of Web-based search engines. Currently, these goals are accomplished by associating metadata information to each image in the database. In this paper, we investigate the use of network sciences to implement a metadata-propagation mechanism for images. We begin by creating a network of images connected by a part-based appearance-similarity measure, and propose an epidemiology-inspired model for metadata propagation. Our experiments show that organizing images as a network helps us label a large number images in the dataset in an economical way, i.e., with few manual metadata annotations.
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Bohlool, M., Menezes, R., Ribeiro, E. (2011). A Network-Centric Epidemic Approach for Automated Image Label Annotation. In: da F. Costa, L., Evsukoff, A., Mangioni, G., Menezes, R. (eds) Complex Networks. Communications in Computer and Information Science, vol 116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25501-4_14
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DOI: https://doi.org/10.1007/978-3-642-25501-4_14
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