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Empirical evaluation of dissimilarity measures for 3D object retrieval with application to multi-feature retrieval

Empirical evaluation of dissimilarity measures for 3D object retrieval with application to multi-feature retrieval

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GREGOR, Robert, Andreas LAMPRECHT, Ivan SIPIRAN, Tobias SCHRECK, Benjamin BUSTOS, 2015. Empirical evaluation of dissimilarity measures for 3D object retrieval with application to multi-feature retrieval. 13th International Workshop on Content-Based Multimedia Indexing (CBMI) 2015. Prague, Czech Republic, 10. Jun 2015 - 12. Jun 2015. In: 13th International Workshop on Content-Based Multimedia Indexing (CBMI) 2015. 13th International Workshop on Content-Based Multimedia Indexing (CBMI) 2015. Prague, Czech Republic, 10. Jun 2015 - 12. Jun 2015. IEEE, pp. 1-6. ISBN 978-1-4673-6870-4. Available under: doi: 10.1109/CBMI.2015.7153629

@inproceedings{Gregor2015Empir-33034, title={Empirical evaluation of dissimilarity measures for 3D object retrieval with application to multi-feature retrieval}, year={2015}, doi={10.1109/CBMI.2015.7153629}, isbn={978-1-4673-6870-4}, publisher={IEEE}, booktitle={13th International Workshop on Content-Based Multimedia Indexing (CBMI) 2015}, pages={1--6}, author={Gregor, Robert and Lamprecht, Andreas and Sipiran, Ivan and Schreck, Tobias and Bustos, Benjamin} }

2016-02-18T10:54:26Z Bustos, Benjamin 2016-02-18T10:54:26Z eng Schreck, Tobias Sipiran, Ivan Lamprecht, Andreas A common approach for implementing content-based multimedia retrieval tasks resorts to extracting high-dimensional feature vectors from the multimedia objects. In combination with an appropriate dissimilarity function, such as the well-known L<sub>p</sub> functions or statistical measures like χ<sup>2</sup>, one can rank objects by dissimilarity with respect to a query. For many multimedia retrieval problems, a large number of feature extraction methods have been proposed and experimentally evaluated for their effectiveness. Much less work has been done to systematically study the impact of the choice of dissimilarity function on the retrieval effectiveness. Inspired by previous work which compared dissimilarity functions for image retrieval, we provide an extensive comparison of dissimilarity measures for 3D object retrieval. Our study is based on an encompassing set of feature extractors, dissimilarity measures and benchmark data sets. We identify the best performing dissimilarity measures and in turn identify dependencies between well-performing dissimilarity measures and types of 3D features. Based on these findings, we show that the effectiveness of 3D retrieval can be improved by a feature-dependent measure choice. In addition, we apply different normalization schemes to the dissimilarity distributions in order to show improved retrieval effectiveness for late fusion of multi-feature combination. Finally, we present preliminary findings on the correlation of rankings for dissimilarity measures, which could be exploited for further improvement of retrieval effectiveness for single features as well as combinations. Bustos, Benjamin Lamprecht, Andreas Sipiran, Ivan 2015 Gregor, Robert Gregor, Robert Empirical evaluation of dissimilarity measures for 3D object retrieval with application to multi-feature retrieval Schreck, Tobias

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