Retrieval evaluation and distance learning from perceived similarity between endomicroscopy videos

Barbara André, Tom Vercauteren, Anna M. Buchner, Michael B. Wallace, Nicholas Ayache

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

10 Scopus citations

Abstract

Evaluating content-based retrieval (CBR) is challenging because it requires an adequate ground-truth. When the available ground-truth is limited to textual metadata such as pathological classes, retrieval results can only be evaluated indirectly, for example in terms of classification performance. In this study we first present a tool to generate perceived similarity ground-truth that enables direct evaluation of endomicroscopic video retrieval. This tool uses a four-points Likert scale and collects subjective pairwise similarities perceived by multiple expert observers. We then evaluate against the generated ground-truth a previously developed dense bag-of-visual-words method for endomicroscopic video retrieval. Confirming the results of previous indirect evaluation based on classification, our direct evaluation shows that this method significantly outperforms several other state-of-the-art CBR methods. In a second step, we propose to improve the CBR method by learning an adjusted similarity metric from the perceived similarity ground-truth. By minimizing a margin-based cost function that differentiates similar and dissimilar video pairs, we learn a weight vector applied to the visual word signatures of videos. Using cross-validation, we demonstrate that the learned similarity distance is significantly better correlated with the perceived similarity than the original visual-word-based distance.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings
Pages297-304
Number of pages8
EditionPART 3
DOIs
StatePublished - Oct 11 2011
Event14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada
Duration: Sep 18 2011Sep 22 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume6893 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011
CountryCanada
CityToronto, ON
Period9/18/119/22/11

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    André, B., Vercauteren, T., Buchner, A. M., Wallace, M. B., & Ayache, N. (2011). Retrieval evaluation and distance learning from perceived similarity between endomicroscopy videos. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings (PART 3 ed., pp. 297-304). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6893 LNCS, No. PART 3). https://doi.org/10.1007/978-3-642-23626-6_37