Automatic summarization of changes in image sequences using algorithmic information theory

Andrew R. Cohen, Christopher Bjornsson, Ying Chen, Gary Banker, Ena Ladi, Ellen Robey, Sally Temple, Badrinath Roysam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

An algorithmic information theoretic method is presented for object-level summarization of meaningful changes in image sequences. Object extraction and tracking data are represented as an attributed tracking graph (ATG), whose connected subgraphs are compared using an adaptive information distance measure, aided by a closed-form multi-dimensional quantization. The summary is the clustering result and feature subset that maximize the gap statistic. The notion of meaningful summarization is captured by using the gap statistic to estimate the randomness deficiency from algorithmic statistics. When applied to movies of cultured neural progenitor cells, it correctly distinguished neurons from progenitors without requiring the use of a fixative stain. When analyzing intra-cellular molecular transport in cultured neurons undergoing axon specification, it automatically confirmed the role of kinesins in axon specification. Finally, it was able to differentiate wild type from genetically modified thymocyte cells.

Original languageEnglish (US)
Title of host publication2008 5th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, Proceedings, ISBI
Pages859-862
Number of pages4
DOIs
StatePublished - Sep 10 2008
Event2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI - Paris, France
Duration: May 14 2008May 17 2008

Publication series

Name2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI

Other

Other2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI
CountryFrance
CityParis
Period5/14/085/17/08

Keywords

  • Algorithmic information theory
  • Algorithmic statistics
  • Clustering
  • Gap statistic
  • Information distance

ASJC Scopus subject areas

  • Biomedical Engineering

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  • Cite this

    Cohen, A. R., Bjornsson, C., Chen, Y., Banker, G., Ladi, E., Robey, E., Temple, S., & Roysam, B. (2008). Automatic summarization of changes in image sequences using algorithmic information theory. In 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI (pp. 859-862). [4541132] (2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI). https://doi.org/10.1109/ISBI.2008.4541132