Visual precis generation using coresets
Author(s)Paul, Rohan; Feldman, Dan; Newman, Paul; Rus, Daniela L.
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Given an image stream, our on-line algorithm will select the semantically-important images that summarize the visual experience of a mobile robot. Our approach consists of data pre-clustering using coresets followed by a graph based incremental clustering procedure using a topic based image representation. A coreset for an image stream is a set of representative images that semantically compresses the data corpus, in the sense that every frame has a similar representative image in the coreset. We prove that our algorithm efficiently computes the smallest possible coreset under natural well-defined similarity metric and up to provably small approximation factor. The output visual summary is computed via a hierarchical tree of coresets for different parts of the image stream. This allows multi-resolution summarization (or a video summary of specified duration) in the batch setting and a memory-efficient incremental summary for the streaming case.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA)
Paul, Rohan, Dan Feldman, Daniela Rus, and Paul Newman. “Visual Precis Generation Using Coresets.” 2014 IEEE International Conference on Robotics and Automation (ICRA) (May 2014).
Author's final manuscript