Abstract
Given a single face image of a specific person as a query, it is very difficult to retrieve all of his/her images from a personal image collection stored for a long term due to age-related changes in facial appearances. This paper proposes to apply relevance feedback to enhance the performance of image retrieval from the image collections with age variation. Specifically, we propose two types of update schemes: i) query expansion and ii) weight updating and show the effects of each scheme by experiments with two actual image collections. For an image collection, the recall rate improved from 40.8% to 72.5% after five iterations of relevance feedback.
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Nitta, N., Usui, A., Babaguchi, N. (2010). Face Image Retrieval across Age Variation Using Relevance Feedback. In: Boll, S., Tian, Q., Zhang, L., Zhang, Z., Chen, YP.P. (eds) Advances in Multimedia Modeling. MMM 2010. Lecture Notes in Computer Science, vol 5916. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11301-7_18
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DOI: https://doi.org/10.1007/978-3-642-11301-7_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11300-0
Online ISBN: 978-3-642-11301-7
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