Abstract
This paper concentrates on the user-centered search task at ImageCLEF 2004. In this work, we combine both textual and visual features for cross-language image retrieval, and propose two interactive retrieval systems – T_ICLEF and VCT_ICLEF. The first one incorporates a relevance feedback mechanism based on textual information while the second one combines textual and image information to help users find a target image. The experimental results show that VCT_ICLEF had a better performance in almost all cases. Overall, it helped users find the topic image within a fewer iterations with a maximum of 2 iterations saved. Our user survey also reported that a combination of textual and visual information is helpful to indicate to the system what a user really wanted in mind.
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© 2005 Springer-Verlag Berlin Heidelberg
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Cheng, PC., Yeh, JY., Ke, HR., Chien, BC., Yang, WP. (2005). Comparison and Combination of Textual and Visual Features for Interactive Cross-Language Image Retrieval. In: Peters, C., Clough, P., Gonzalo, J., Jones, G.J.F., Kluck, M., Magnini, B. (eds) Multilingual Information Access for Text, Speech and Images. CLEF 2004. Lecture Notes in Computer Science, vol 3491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11519645_77
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DOI: https://doi.org/10.1007/11519645_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27420-9
Online ISBN: 978-3-540-32051-7
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