Circular Earth Mover’s Distance for the comparison of local features
作者： Julien Rabin , Julie Delon , Yann Gousseau
DOI: 10.1109/ICPR.2008.4761372
关键词: Earth mover's distance 、 Time complexity 、 Computational complexity theory 、 Feature extraction 、 Scale-invariant feature transform 、 Artificial intelligence 、 Computer vision algorithms 、 Histogram 、 Robustness (computer science) 、 Mathematics 、 Pattern recognition
摘要: Many computer vision algorithms make use of local features, and rely on a systematic comparison these features. The chosen dissimilarity measure is crucial importance for the overall performances has to be both robust computationally efficient. Some most popular features (like SIFT [4] descriptors) are based one-dimensional circular histograms. In this contribution, we present an adaptation Earth moverpsilas distance This distance, that call CEMD, used compare SIFT-like descriptors. Experiments over large database 3 million descriptors show CEMD outperforms classical bin-to-bin distances, while having reasonable time complexity.
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