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- W1973282963 abstract "This demonstration illustrates a service for collection and delivery of images, in participatory camera networks, to maximize coverage while removing outliers (i.e., irrelevant images). Images, such as those taken by smart-phone users, represent an important and growing modality in social sensing applications. They can be used, for instance, to document occurrences of interest in participatory sensing cam-paigns, such as instances of graffiti on campus or invasive species in a park. In applications with a significant number of participants, the number of images collected may be very large. A key problem becomes one of data triage to reduce the number of images delivered to a manageable count, without missing important ones. In prior work, the authors presented a service, called PhotoNet [2], that reduces redundancy among delivered images by maximizing diversity. The current work significantly extends our previous effort by recognizing that diversity maximization often leads to selection of outliers; images that are visually different but not necessarily relevant, which in fact reduces the quality of the delivered image pool. We demonstrate a new prioritization technique that maximizes diversity among delivered pictures, while also reducing outliers." @default.
- W1973282963 created "2016-06-24" @default.
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- W1973282963 date "2012-04-01" @default.
- W1973282963 modified "2023-09-23" @default.
- W1973282963 title "Demo abstract: PhotoNet+: Outlier-resilient coverage maximization in visual sensing applications" @default.
- W1973282963 cites W2167339613 @default.
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- W1973282963 doi "https://doi.org/10.1109/ipsn.2012.6920937" @default.
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