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