Algebraic Set Kernels with Application to Inference over Local Image Representations
Abstract
This paper presents a general family of algebraic positive definite simi- larity functions over spaces of matrices with varying column rank. The columns can represent local regions in an image (whereby images have varying number of local parts), images of an image sequence, motion tra- jectories in a multibody motion, and so forth. The family of set kernels we derive is based on a group invariant tensor product lifting with param- eters that can be naturally tuned to provide a cook-book of sorts covering the possible "wish lists" from similarity measures over sets of varying cardinality. We highlight the strengths of our approach by demonstrat- ing the set kernels for visual recognition of pedestrians using local parts representations.
Cite
Text
Shashua and Hazan. "Algebraic Set Kernels with Application to Inference over Local Image Representations." Neural Information Processing Systems, 2004.Markdown
[Shashua and Hazan. "Algebraic Set Kernels with Application to Inference over Local Image Representations." Neural Information Processing Systems, 2004.](https://mlanthology.org/neurips/2004/shashua2004neurips-algebraic/)BibTeX
@inproceedings{shashua2004neurips-algebraic,
title = {{Algebraic Set Kernels with Application to Inference over Local Image Representations}},
author = {Shashua, Amnon and Hazan, Tamir},
booktitle = {Neural Information Processing Systems},
year = {2004},
pages = {1257-1264},
url = {https://mlanthology.org/neurips/2004/shashua2004neurips-algebraic/}
}