Feynman Liang created SPARK-8493:
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Summary: 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
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++.
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