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Retrieval evaluation and distance learning from perceived similarity between endomicroscopy videos

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.
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Contributor : Barbara André <>
Submitted on : Monday, June 6, 2011 - 10:26:18 AM
Last modification on : Thursday, August 22, 2019 - 11:02:36 AM
Document(s) archivé(s) le : Wednesday, September 7, 2011 - 9:51:59 AM


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Barbara André, Tom Vercauteren, Anna Buchner, Michael Wallace, Nicholas Ayache. Retrieval evaluation and distance learning from perceived similarity between endomicroscopy videos. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2011, 2011, Toronto, Canada. pp.297-304, ⟨10.1007/978-3-642-23626-6_37⟩. ⟨inria-00598301⟩



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