Demo abstract: PhotoNet+: Outlier-resilient coverage maximization in visual sensing applications

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.