Abstract
An approach to automatic image annotation is proposed. Generally, the relation between visual characteristics and the annotation label is estimated from the annotated corpus and is used to predict label for new test image. Unfortunately, when limited number of images are annotated, with possible multiple labels per image, this relation cannot be reliably estimated. To cope with this problem, we propose taking into account information derived directly from other images in the dataset. This method extends naturally to semi-supervised setting where un-annotated images are also used select annotation labels. Experiment shows that the proposed method yields promising results.
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Marukatat, S. (2008). A New Model for Image Annotation. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2008. Lecture Notes in Computer Science(), vol 5012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68125-0_99
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DOI: https://doi.org/10.1007/978-3-540-68125-0_99
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68124-3
Online ISBN: 978-3-540-68125-0
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