The Minimum Selection of Crowdsourcing Images under the Resource Budget
AbstractImages crowdsourcing of mobile devices can be applied to many real-life application scenarios. However, this type of scenario application often faces issues such as the limitation of bandwidth, insufficient storage space, and the processing capability of CPU. These lead to only a few photos that can be crowdsourced. Therefore, it is a great challenge to use a limited number of resources to select photos and make it possible to cover the target area maximally. In this paper, the geographic and geometric information of the photo called data-unit is used to cover the target area as much as possible. Compared with traditional content-based image delivery methods, the network delay and computational costs can be greatly reduced. In the case of resource constraints, this paper uses the utility of photos to measure the coverage of the target area, and improves a photo utility calculation method based on data-unit. In the meantime, this paper proposes the minimum selection problem of images under the coverage requirements, and designs a selection algorithm based on greedy strategies. Compared with other traditional random selection algorithms, the results prove the effectiveness and superiority of the minimum selection algorithm. View Full-Text
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Song, J.; Zhao, M.; Long, S. The Minimum Selection of Crowdsourcing Images under the Resource Budget. Symmetry 2018, 10, 256.
Song J, Zhao M, Long S. The Minimum Selection of Crowdsourcing Images under the Resource Budget. Symmetry. 2018; 10(7):256.Chicago/Turabian Style
Song, Jieqiong; Zhao, Ming; Long, Sifan. 2018. "The Minimum Selection of Crowdsourcing Images under the Resource Budget." Symmetry 10, no. 7: 256.
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