Abstract
In recent years, estimating the locations of images has received a lot of attention, which plays a role in application scenarios for large geo-tagged image corpora. So, as to images which are not geographically tagged, we could estimate their locations with the help of the large geo-tagged image set by visual mining based approach. In this paper, we propose a global feature clustering and local feature refinement based image location estimation approach. Firstly, global feature clustering is utilized. We further treat each cluster as a single observation. Next we mine the relationship of each image cluster and locations offline. By cluster selection online, several refined locations likely to be related to an input image are pre-selected. Secondly, we localize the input image by local feature matching which utilizes the “SIFT” descriptor extracted from the refined images. In this process, “spatial layers of visual word” (SLW) is built as an extension of the unorganized bag-of-words image representation. Experiments show the effectiveness of our proposed approach.
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Zhao, Y., Qian, X., Mu, T. (2015). Image Taken Place Estimation via Geometric Constrained Spatial Layer Matching. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds) MultiMedia Modeling. MMM 2015. Lecture Notes in Computer Science, vol 8936. Springer, Cham. https://doi.org/10.1007/978-3-319-14442-9_49
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DOI: https://doi.org/10.1007/978-3-319-14442-9_49
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14441-2
Online ISBN: 978-3-319-14442-9
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