@inproceedings{de66c346ac8e4c9889a9839868a25b1a,
title = "Visual precis generation using coresets",
abstract = "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.",
author = "Rohan Paul and Dan Feldman and Daniela Rus and Paul Newman",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE International Conference on Robotics and Automation, ICRA 2014 ; Conference date: 31-05-2014 Through 07-06-2014",
year = "2014",
month = sep,
day = "22",
doi = "https://doi.org/10.1109/ICRA.2014.6907021",
language = "American English",
series = "Proceedings - IEEE International Conference on Robotics and Automation",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1304--1311",
booktitle = "IEEE International Conference on Robotics and Automation (ICRA) 2014",
address = "United States",
}