- Authors:
- Scott Cohen,
- Leonidas Guibas
The Earth Mover''s Distance (EMD) between two finite distributions of weight is proportional to the minimum amount of work required to transform one distribution into the other. Current content-based retrieval work in the Stanford Vision Laboratory uses the EMD as a common framework for measuring image similarity with respect to color, texture, and shape content. In this report, we present some fast to compute lower bounds on the EMD which may allow a system to avoid exact, more expensive EMD computations during query processing. The effectiveness of the lower bounds is tested in a color-based retrieval system. In addition to the lower bound work, we also show how to compute the EMD under translation. In this problem, the points in one distribution are free to translate, and the goal is to find a translation that minimizes the EMD to the other distribution.
Cited By
Levi G and Hassner T Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, (503-510)- Tang Y, U L, Cai Y, Mamoulis N and Cheng R (2013). Earth mover's distance based similarity search at scale, Proceedings of the VLDB Endowment, 7:4, (313-324), Online publication date: 1-Dec-2013.
- Charikar M Similarity estimation techniques from rounding algorithms Proceedings of the thiry-fourth annual ACM symposium on Theory of computing, (380-388)
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