Image Data Compression for Covariance and Histogram Descriptors

12/04/2014
by   Matt J. Kusner, et al.
0

Covariance and histogram image descriptors provide an effective way to capture information about images. Both excel when used in combination with special purpose distance metrics. For covariance descriptors these metrics measure the distance along the non-Euclidean Riemannian manifold of symmetric positive definite matrices. For histogram descriptors the Earth Mover's distance measures the optimal transport between two histograms. Although more precise, these distance metrics are very expensive to compute, making them impractical in many applications, even for data sets of only a few thousand examples. In this paper we present two methods to compress the size of covariance and histogram datasets with only marginal increases in test error for k-nearest neighbor classification. Specifically, we show that we can reduce data sets to 16 while approximately matching the test error of kNN classification on the full training set. In fact, because the compressed set is learned in a supervised fashion, it sometimes even outperforms the full data set, while requiring only a fraction of the space and drastically reducing test-time computation.

READ FULL TEXT

page 5

page 7

research
09/16/2019

More About Covariance Descriptors for Image Set Coding: Log-Euclidean Framework based Kernel Matrix Representation

We consider a family of structural descriptors for visual data, namely c...
research
10/31/2016

A New Distance Measure for Non-Identical Data with Application to Image Classification

Distance measures are part and parcel of many computer vision algorithms...
research
11/24/2016

Comparative study of histogram distance measures for re-identification

Color based re-identification methods usually rely on a distance functio...
research
12/08/2015

Is Hamming distance the only way for matching binary image feature descriptors?

Brute force matching of binary image feature descriptors is conventional...
research
06/14/2017

Hierarchical Gaussian Descriptors with Application to Person Re-Identification

Describing the color and textural information of a person image is one o...
research
12/24/2020

Correlated Wishart Matrices Classification via an Expectation-Maximization Composite Likelihood-Based Algorithm

Positive-definite matrix-variate data is becoming popular in computer vi...

Please sign up or login with your details

Forgot password? Click here to reset