A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf
.
Fisher Vectors Derived from Hybrid Gaussian-Laplacian Mixture Models for Image Annotation
[article]
2015
arXiv
pre-print
In the traditional object recognition pipeline, descriptors are densely sampled over an image, pooled into a high dimensional non-linear representation and then passed to a classifier. In recent years, Fisher Vectors have proven empirically to be the leading representation for a large variety of applications. The Fisher Vector is typically taken as the gradients of the log-likelihood of descriptors, with respect to the parameters of a Gaussian Mixture Model (GMM). Motivated by the assumption
arXiv:1411.7399v2
fatcat:kjzlvvzcvfhjnirvpehdes6hx4