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https://issues.apache.org/jira/browse/SPARK-8486?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Feynman Liang updated SPARK-8486:
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Summary: SIFT/SURF Feature Transformer (was: SIFT/SURF Feature Extractor)
> SIFT/SURF Feature Transformer
> -----------------------------
>
> Key: SPARK-8486
> URL: https://issues.apache.org/jira/browse/SPARK-8486
> Project: Spark
> Issue Type: Sub-task
> Components: ML
> Reporter: Feynman Liang
>
> Scale invariant feature transform (SIFT) is a method to transform images into
> dense vectors describing local features which are invariant to scale and
> rotation. (Lowe, IJCV 2004, http://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf)
> We can implement SIFT in Spark ML pipelines as a
> org.apache.spark.ml.Transformer. Given an image Array[Array[Numeric]], the
> SIFT transformer should output an Array[Numeric] of the SIFT features present
> in the image.
> Depending on performance, approximating Laplacian of Gaussian by Difference
> of Gaussian (traditional SIFT) as described by Lowe can be even further
> improved using box filters (aka SURF, see Bay, ECCV 2006,
> http://www.vision.ee.ethz.ch/~surf/eccv06.pdf).
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