Abstract
This paper presents a method of indexing video stored from live nature cameras on digital Earth and its application. Since two feature vectors from scene images are not necessary identical in the metrical space even if they are similar, it is hard to get benefit of indexing feature vectors. That is, the search system consequently has to fully access to almost whole feature vectors in the storage. The feature of the proposed system is that for indexing feature vectors, similarity-based indexing by clustering feature vectors is applied, where the feature vectore can be indexed with a centorid vector of the corresponding cluster. Consequently, the computing cost for accsessing to the objective images can be majorly reduced without the almost full access to whole feature vectors. For making our concept clear, we show an application scenario using video data from live streaming cameras that are opened to the public and some aerial or satellite images.
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Mimura, H., Tahara, M., Takano, K., Watanabe, N., Li, K.F. (2023). Video Indexing for Live Nature Camera on Digital Earth. In: Barolli, L. (eds) Advanced Information Networking and Applications. AINA 2023. Lecture Notes in Networks and Systems, vol 655. Springer, Cham. https://doi.org/10.1007/978-3-031-28694-0_62
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DOI: https://doi.org/10.1007/978-3-031-28694-0_62
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