[
https://issues.apache.org/jira/browse/SPARK-8493?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Feynman Liang updated SPARK-8493:
---------------------------------
Priority: Minor (was: Major)
> Fisher Vector Feature Transformer
> ---------------------------------
>
> Key: SPARK-8493
> URL: https://issues.apache.org/jira/browse/SPARK-8493
> Project: Spark
> Issue Type: Sub-task
> Components: ML
> Reporter: Feynman Liang
> Priority: Minor
>
> Fisher vectors provide a vocabulary-based encoding for images (see
> https://hal.inria.fr/hal-00830491/file/journal.pdf). This representation is
> useful due to reduced dimensionality, providing regularization as well as
> increased scalability.
> An implementation of FVs in Spark ML should provide a way to both train a GMM
> vocabulary as well compute Fisher kernel encodings of provided images. The
> vocabulary trainer can be implemented as a standalone GMM pipeline. The
> feature transformer can be implemented as a
> org.apache.spark.ml.UnaryTransformer. It should accept a vocabulary
> (Array[Array[Double]]) as well as an image (Array[Double]) and produce the
> Fisher kernel encoding (Array[Double]).
> See Enceval (http://www.robots.ox.ac.uk/~vgg/software/enceval_toolkit/) for a
> reference implementation in MATLAB/C++.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
