Improving shape retrieval by learning graph transduction
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 5305 LNCS, Issue: PART 4, Page: 788-801
2008
- 122Citations
- 56Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Shape retrieval/matching is a very important topic in computer vision. The recent progress in this domain has been mostly driven by designing smart features for providing better similarity measure between pairs of shapes. In this paper, we provide a new perspective to this problem by considering the existing shapes as a group, and study their similarity measures to the query shape in a graph structure. Our method is general and can be built on top of any existing shape matching algorithms. It learns a better metric through graph transduction by propagating the model through existing shapes, in a way similar to computing geodesics in shape manifold. However, the proposed method does not require learning the shape manifold explicitly and it does not require knowing any class labels of existing shapes. The presented experimental results demonstrate that the proposed approach yields significant improvements over the state-of-art shape matching algorithms. We obtained a retrieval rate of 91% on the MPEG-7 data set, which is the highest ever reported in the literature. © 2008 Springer Berlin Heidelberg.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=56749153656&origin=inward; http://dx.doi.org/10.1007/978-3-540-88693-8_58; http://link.springer.com/10.1007/978-3-540-88693-8_58; https://doi.org/10.1007%2F978-3-540-88693-8_58; https://dx.doi.org/10.1007/978-3-540-88693-8_58; https://link.springer.com/chapter/10.1007/978-3-540-88693-8_58; http://www.springerlink.com/index/10.1007/978-3-540-88693-8_58; http://www.springerlink.com/index/pdf/10.1007/978-3-540-88693-8_58
Springer Science and Business Media LLC
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