Visual precis generation using coresets

Rohan Paul, Dan Feldman, Daniela Rus, Paul Newman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageAmerican English
Title of host publicationIEEE International Conference on Robotics and Automation (ICRA) 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1304-1311
Number of pages8
ISBN (Electronic)9781479936854
DOIs
StatePublished - 22 Sep 2014
Externally publishedYes
Event2014 IEEE International Conference on Robotics and Automation, ICRA 2014 - Hong Kong, China
Duration: 31 May 20147 Jun 2014

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation

Conference

Conference2014 IEEE International Conference on Robotics and Automation, ICRA 2014
Country/TerritoryChina
CityHong Kong
Period31/05/147/06/14

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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