On measuring the distance between histograms

By Sung-Hyuk Cha, Sargur N. Srihari. Pattern Recognition, 2002

441 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/75d0f2a4a2dff5cce4eb4097c2e68e1ff75ae569

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

A Normalized Levenshtein Distance Metric (2007, methodology): “Moreover, we will also consider the problems of how to use the presented techniques to normalize a distance between histograms [23], [24] or a local alignment score between strings [25], [26] in a provably more rigorous way.”
Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions (2007, background): “There are different types of histograms [6].”
A fully automatic end-to-end method for content-based image retrieval of CT scans with similar liver lesion annotations (2017, methodology): “Despite its simplicity and exclusive reliance on low-level image features, this approach is computationally efficient and has been reported to yields good results in practice for image indexing and retrieval tasks [26].”
Proposal : Scalable , Active and Flexible Learning on Distributions (2015, unknown): “When the test values are not available, Chen et al. (2009) propose a heuristic to account for the effect of Π: they find the linear transformation which maps K̂train to ΠK̂train, based on the eigendecomposition of K̂train, and apply it to K̂test,train.”
Histograms of oriented optical flow and Binet-Cauchy kernels on nonlinear dynamical systems for the recognition of human actions (2009, methodology): “Minimum Difference of Pairwise Assignment (MDPA) [5] is similar to the Earth Mover’s Distance (EMD) [22] and is a metric on the space of histograms that implicitly is a summation of distances between points on an underlying metric space from which the histograms were created.”
A Linear Time Histogram Metric for Improved SIFT Matching (2008, background): “Cha and Srihari [27] rediscovered these algorithms and described a character writer identiﬁcation application.”