Abstract
The SenseCam is a wearable camera that automatically takes photos of the wearer’s activities, generating thousands of images per day. Automatically organising these images for efficient search and retrieval is a challenging task, but can be simplified by providing semantic information with each photo, such as the wearer’s location during capture time. We propose a method for automatically determining the wearer’s location using an annotated image database, described using SURF interest point descriptors. We show that SURF out-performs SIFT in matching SenseCam images and that matching can be done efficiently using hierarchical trees of SURF descriptors. Additionally, by re-ranking the top images using bi-directional SURF matches, location matching performance is improved further.
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Conaire, C.Ó., Blighe, M., O’Connor, N.E. (2009). SenseCam Image Localisation Using Hierarchical SURF Trees. In: Huet, B., Smeaton, A., Mayer-Patel, K., Avrithis, Y. (eds) Advances in Multimedia Modeling . MMM 2009. Lecture Notes in Computer Science, vol 5371. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92892-8_4
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DOI: https://doi.org/10.1007/978-3-540-92892-8_4
Publisher Name: Springer, Berlin, Heidelberg
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