A Network-Centric Epidemic Approach for Automated Image Label Annotation

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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