The Earth Mover’s Distance - Beyond Nearest Neighbor Classification ?
نویسندگان
چکیده
Histograms are ubiquitous tools in numerous machine learning and computer vision tasks. It is common practice to use distances such as L2 for comparing histograms. This practice assumes that the histogram domains are aligned. However this assumption is violated through quantization, shape deformation, light changes, etc. The Earth Mover’s Distance (EMD) [12] is a cross-bin distance that addresses this alignment problem. EMD is defined as the minimal cost that must be paid to transform one histogram into the other, where there is a “ground distance” between the basic features that are aggregated into the histogram:
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