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Posted to issues@spark.apache.org by "Feynman Liang (JIRA)" <ji...@apache.org> on 2015/06/22 19:41:01 UTC
[jira] [Updated] (SPARK-8493) Fisher Vector Estimator
[ https://issues.apache.org/jira/browse/SPARK-8493?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Feynman Liang updated SPARK-8493:
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Description:
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 an `estimator`) as well compute Fisher kernel encodings of provided images (as a `transformer`). 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++.
was:
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++.
Summary: Fisher Vector Estimator (was: Fisher Vector Feature Transformer)
> Fisher Vector Estimator
> -----------------------
>
> 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 an `estimator`) as well compute Fisher kernel encodings of provided images (as a `transformer`). 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++.
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