Abstract
In this paper, we propose a novel method for cross-media semantic-based information retrieval, which combines classical text- based and content-based image retrieval techniques. This semantic-based approach aims at determining the strong relationships between keywords (in the caption) and types of visual features associated with its typical images. These relationships are then used to retrieve images from a textual query. In particular, the association keyword/visual feature may allow us to retrieve non-annotated but similar images to those retrieved by a classical textual query. It can also be used for automatic images annotation. Our experiments on two different databases show that this approach is promising for cross-media retrieval.
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Oumohmed, A.I., Mignotte, M., Nie, JY. (2005). Semantic-Based Cross-Media Image Retrieval. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_47
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DOI: https://doi.org/10.1007/11552499_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28833-6
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